how long to write up phd

Tips for writing a PhD dissertation: FAQs answered

From how to choose a topic to writing the abstract and managing work-life balance through the years it takes to complete a doctorate, here we collect expert advice to get you through the PhD writing process

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Embarking on a PhD is “probably the most challenging task that a young scholar attempts to do”, write Mark Stephan Felix and Ian Smith in their practical guide to dissertation and thesis writing. After years of reading and research to answer a specific question or proposition, the candidate will submit about 80,000 words that explain their methods and results and demonstrate their unique contribution to knowledge. Here are the answers to frequently asked questions about writing a doctoral thesis or dissertation.

What’s the difference between a dissertation and a thesis?

Whatever the genre of the doctorate, a PhD must offer an original contribution to knowledge. The terms “dissertation” and “thesis” both refer to the long-form piece of work produced at the end of a research project and are often used interchangeably. Which one is used might depend on the country, discipline or university. In the UK, “thesis” is generally used for the work done for a PhD, while a “dissertation” is written for a master’s degree. The US did the same until the 1960s, says Oxbridge Essays, when the convention switched, and references appeared to a “master’s thesis” and “doctoral dissertation”. To complicate matters further, undergraduate long essays are also sometimes referred to as a thesis or dissertation.

The Oxford English Dictionary defines “thesis” as “a dissertation, especially by a candidate for a degree” and “dissertation” as “a detailed discourse on a subject, especially one submitted in partial fulfilment of the requirements of a degree or diploma”.

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The title “doctor of philosophy”, incidentally, comes from the degree’s origins, write Dr Felix, an associate professor at Mahidol University in Thailand, and Dr Smith, retired associate professor of education at the University of Sydney , whose co-authored guide focuses on the social sciences. The PhD was first awarded in the 19th century by the philosophy departments of German universities, which at that time taught science, social science and liberal arts.

How long should a PhD thesis be?

A PhD thesis (or dissertation) is typically 60,000 to 120,000 words ( 100 to 300 pages in length ) organised into chapters, divisions and subdivisions (with roughly 10,000 words per chapter) – from introduction (with clear aims and objectives) to conclusion.

The structure of a dissertation will vary depending on discipline (humanities, social sciences and STEM all have their own conventions), location and institution. Examples and guides to structure proliferate online. The University of Salford , for example, lists: title page, declaration, acknowledgements, abstract, table of contents, lists of figures, tables and abbreviations (where needed), chapters, appendices and references.

A scientific-style thesis will likely need: introduction, literature review, materials and methods, results, discussion, bibliography and references.

As well as checking the overall criteria and expectations of your institution for your research, consult your school handbook for the required length and format (font, layout conventions and so on) for your dissertation.

A PhD takes three to four years to complete; this might extend to six to eight years for a part-time doctorate.

What are the steps for completing a PhD?

Before you get started in earnest , you’ll likely have found a potential supervisor, who will guide your PhD journey, and done a research proposal (which outlines what you plan to research and how) as part of your application, as well as a literature review of existing scholarship in the field, which may form part of your final submission.

In the UK, PhD candidates undertake original research and write the results in a thesis or dissertation, says author and vlogger Simon Clark , who posted videos to YouTube throughout his own PhD journey . Then they submit the thesis in hard copy and attend the viva voce (which is Latin for “living voice” and is also called an oral defence or doctoral defence) to convince the examiners that their work is original, understood and all their own. Afterwards, if necessary, they make changes and resubmit. If the changes are approved, the degree is awarded.

The steps are similar in Australia , although candidates are mostly assessed on their thesis only; some universities may include taught courses, and some use a viva voce. A PhD in Australia usually takes three years full time.

In the US, the PhD process begins with taught classes (similar to a taught master’s) and a comprehensive exam (called a “field exam” or “dissertation qualifying exam”) before the candidate embarks on their original research. The whole journey takes four to six years.

A PhD candidate will need three skills and attitudes to get through their doctoral studies, says Tara Brabazon , professor of cultural studies at Flinders University in Australia who has written extensively about the PhD journey :

• master the academic foundational skills (research, writing, ability to navigate different modalities)
• time-management skills and the ability to focus on reading and writing
• determined motivation to do a PhD.

How do I choose the topic for my PhD dissertation or thesis?

It’s important to find a topic that will sustain your interest for the years it will take to complete a PhD. “Finding a sustainable topic is the most important thing you [as a PhD student] would do,” says Dr Brabazon in a video for Times Higher Education . “Write down on a big piece of paper all the topics, all the ideas, all the questions that really interest you, and start to cross out all the ones that might just be a passing interest.” Also, she says, impose the “Who cares? Who gives a damn?” question to decide if the topic will be useful in a future academic career.

The availability of funding and scholarships is also often an important factor in this decision, says veteran PhD supervisor Richard Godwin, from Harper Adams University .

Define a gap in knowledge – and one that can be questioned, explored, researched and written about in the time available to you, says Gina Wisker, head of the Centre for Learning and Teaching at the University of Brighton. “Set some boundaries,” she advises. “Don’t try to ask everything related to your topic in every way.”

James Hartley, research professor in psychology at Keele University, says it can also be useful to think about topics that spark general interest. If you do pick something that taps into the zeitgeist, your findings are more likely to be noticed.

You also need to find someone else who is interested in it, too. For STEM candidates , this will probably be a case of joining a team of people working in a similar area where, ideally, scholarship funding is available. A centre for doctoral training (CDT) or doctoral training partnership (DTP) will advertise research projects. For those in the liberal arts and social sciences, it will be a matter of identifying a suitable supervisor .

Avoid topics that are too broad (hunger across a whole country, for example) or too narrow (hunger in a single street) to yield useful solutions of academic significance, write Mark Stephan Felix and Ian Smith. And ensure that you’re not repeating previous research or trying to solve a problem that has already been answered. A PhD thesis must be original.

What is a thesis proposal?

After you have read widely to refine your topic and ensure that it and your research methods are original, and discussed your project with a (potential) supervisor, you’re ready to write a thesis proposal , a document of 1,500 to 3,000 words that sets out the proposed direction of your research. In the UK, a research proposal is usually part of the application process for admission to a research degree. As with the final dissertation itself, format varies among disciplines, institutions and countries but will usually contain title page, aims, literature review, methodology, timetable and bibliography. Examples of research proposals are available online.

How to write an abstract for a dissertation or thesis

The abstract presents your thesis to the wider world – and as such may be its most important element , says the NUI Galway writing guide. It outlines the why, how, what and so what of the thesis . Unlike the introduction, which provides background but not research findings, the abstract summarises all sections of the dissertation in a concise, thorough, focused way and demonstrates how well the writer understands their material. Check word-length limits with your university – and stick to them. About 300 to 500 words is a rough guide ­– but it can be up to 1,000 words.

The abstract is also important for selection and indexing of your thesis, according to the University of Melbourne guide , so be sure to include searchable keywords.

It is the first thing to be read but the last element you should write. However, Pat Thomson , professor of education at the University of Nottingham , advises that it is not something to be tackled at the last minute.

How to write a stellar conclusion

As well as chapter conclusions, a thesis often has an overall conclusion to draw together the key points covered and to reflect on the unique contribution to knowledge. It can comment on future implications of the research and open up new ideas emanating from the work. It is shorter and more general than the discussion chapter , says online editing site Scribbr, and reiterates how the work answers the main question posed at the beginning of the thesis. The conclusion chapter also often discusses the limitations of the research (time, scope, word limit, access) in a constructive manner.

It can be useful to keep a collection of ideas as you go – in the online forum DoctoralWriting SIG , academic developer Claire Aitchison, of the University of South Australia , suggests using a “conclusions bank” for themes and inspirations, and using free-writing to keep this final section fresh. (Just when you feel you’ve run out of steam.) Avoid aggrandising or exaggerating the impact of your work. It should remind the reader what has been done, and why it matters.

How to format a bibliography (or where to find a reliable model)

Most universities use a preferred style of references , writes THE associate editor Ingrid Curl. Make sure you know what this is and follow it. “One of the most common errors in academic writing is to cite papers in the text that do not then appear in the bibliography. All references in your thesis need to be cross-checked with the bibliography before submission. Using a database during your research can save a great deal of time in the writing-up process.”

A bibliography contains not only works cited explicitly but also those that have informed or contributed to the research – and as such illustrates its scope; works are not limited to written publications but include sources such as film or visual art.

Examiners can start marking from the back of the script, writes Dr Brabazon. “Just as cooks are judged by their ingredients and implements, we judge doctoral students by the calibre of their sources,” she advises. She also says that candidates should be prepared to speak in an oral examination of the PhD about any texts included in their bibliography, especially if there is a disconnect between the thesis and the texts listed.

Can I use informal language in my PhD?

Don’t write like a stereotypical academic , say Kevin Haggerty, professor of sociology at the University of Alberta , and Aaron Doyle, associate professor in sociology at Carleton University , in their tongue-in-cheek guide to the PhD journey. “If you cannot write clearly and persuasively, everything about PhD study becomes harder.” Avoid jargon, exotic words, passive voice and long, convoluted sentences – and work on it consistently. “Writing is like playing guitar; it can improve only through consistent, concerted effort.”

Be deliberate and take care with your writing . “Write your first draft, leave it and then come back to it with a critical eye. Look objectively at the writing and read it closely for style and sense,” advises THE ’s Ms Curl. “Look out for common errors such as dangling modifiers, subject-verb disagreement and inconsistency. If you are too involved with the text to be able to take a step back and do this, then ask a friend or colleague to read it with a critical eye. Remember Hemingway’s advice: ‘Prose is architecture, not interior decoration.’ Clarity is key.”

How often should a PhD candidate meet with their supervisor?

Since the PhD supervisor provides a range of support and advice – including on research techniques, planning and submission – regular formal supervisions are essential, as is establishing a line of contact such as email if the candidate needs help or advice outside arranged times. The frequency varies according to university, discipline and individual scholars.

Once a week is ideal, says Dr Brabazon. She also advocates a two-hour initial meeting to establish the foundations of the candidate-supervisor relationship .

The University of Edinburgh guide to writing a thesis suggests that creating a timetable of supervisor meetings right at the beginning of the research process will allow candidates to ensure that their work stays on track throughout. The meetings are also the place to get regular feedback on draft chapters.

“A clear structure and a solid framework are vital for research,” writes Dr Godwin on THE Campus . Use your supervisor to establish this and provide a realistic view of what can be achieved. “It is vital to help students identify the true scientific merit, the practical significance of their work and its value to society.”

How to proofread your dissertation (what to look for)

Proofreading is the final step before printing and submission. Give yourself time to ensure that your work is the best it can be . Don’t leave proofreading to the last minute; ideally, break it up into a few close-reading sessions. Find a quiet place without distractions. A checklist can help ensure that all aspects are covered.

Proofing is often helped by a change of format – so it can be easier to read a printout rather than working off the screen – or by reading sections out of order. Fresh eyes are better at spotting typographical errors and inconsistencies, so leave time between writing and proofreading. Check with your university’s policies before asking another person to proofread your thesis for you.

As well as close details such as spelling and grammar, check that all sections are complete, all required elements are included , and nothing is repeated or redundant. Don’t forget to check headings and subheadings. Does the text flow from one section to another? Is the structure clear? Is the work a coherent whole with a clear line throughout?

Ensure consistency in, for example, UK v US spellings, capitalisation, format, numbers (digits or words, commas, units of measurement), contractions, italics and hyphenation. Spellchecks and online plagiarism checkers are also your friend.

How do you manage your time to complete a PhD dissertation?

Treat your PhD like a full-time job, that is, with an eight-hour working day. Within that, you’ll need to plan your time in a way that gives a sense of progress . Setbacks and periods where it feels as if you are treading water are all but inevitable, so keeping track of small wins is important, writes A Happy PhD blogger Luis P. Prieto.

Be specific with your goals – use the SMART acronym (specific, measurable, attainable, relevant and timely).

And it’s never too soon to start writing – even if early drafts are overwritten and discarded.

“ Write little and write often . Many of us make the mistake of taking to writing as one would take to a sprint, in other words, with relatively short bursts of intense activity. Whilst this can prove productive, generally speaking it is not sustainable…In addition to sustaining your activity, writing little bits on a frequent basis ensures that you progress with your thinking. The comfort of remaining in abstract thought is common; writing forces us to concretise our thinking,” says Christian Gilliam, AHSS researcher developer at the University of Cambridge ’s Centre for Teaching and Learning.

Make time to write. “If you are more alert early in the day, find times that suit you in the morning; if you are a ‘night person’, block out some writing sessions in the evenings,” advises NUI Galway’s Dermot Burns, a lecturer in English and creative arts. Set targets, keep daily notes of experiment details that you will need in your thesis, don’t confuse writing with editing or revising – and always back up your work.

What work-life balance tips should I follow to complete my dissertation?

During your PhD programme, you may have opportunities to take part in professional development activities, such as teaching, attending academic conferences and publishing your work. Your research may include residencies, field trips or archive visits. This will require time-management skills as well as prioritising where you devote your energy and factoring in rest and relaxation. Organise your routine to suit your needs , and plan for steady and regular progress.

How to deal with setbacks while writing a thesis or dissertation

Have a contingency plan for delays or roadblocks such as unexpected results.

Accept that writing is messy, first drafts are imperfect, and writer’s block is inevitable, says Dr Burns. His tips for breaking it include relaxation to free your mind from clutter, writing a plan and drawing a mind map of key points for clarity. He also advises feedback, reflection and revision: “Progressing from a rough version of your thoughts to a superior and workable text takes time, effort, different perspectives and some expertise.”

“Academia can be a relentlessly brutal merry-go-round of rejection, rebuttal and failure,” writes Lorraine Hope , professor of applied cognitive psychology at the University of Portsmouth, on THE Campus. Resilience is important. Ensure that you and your supervisor have a relationship that supports open, frank, judgement-free communication.

If you found this interesting and want advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the THE Campus newsletter .

Authoring a PhD Thesis: How to Plan, Draft, Write and Finish a Doctoral Dissertation (2003), by Patrick Dunleavy

Writing Your Dissertation in Fifteen Minutes a Day: A Guide to Starting, Revising, and Finishing Your Doctoral Thesis (1998), by Joan Balker

Challenges in Writing Your Dissertation: Coping with the Emotional, Interpersonal, and Spiritual Struggles (2015), by Noelle Sterne

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Writing up your PhD and Preparing for the Viva

Writing up and submitting your thesis on time should be your priority in your final year, but you should also make time to prepare for your next steps.

Typical milestones

These are the sorts of actions you will need to consider taking during the end phase of your PhD.

Completing your research :

• draw up a plan to cut writing up into manageable pieces
• chapter by chapter; complete a first draft
• submit thesis and practice for the viva
• viva, corrections and graduation....celebrate!

Communicate your findings :

• present research findings at conferences / seminars.

Plan your career :

• Visit the careers service and work on updating your CV.
• Apply for jobs or funding, or think about entrepreneurial activities, like starting your own business or ‘spinning out’ your research.

Remember to add your own additional actions that relate to your own personal circumstances and project.

Support from your supervisor and School

As you near completion, you will be the expert in your field, your relationship with your supervisor has probably changed dramatically since day one. Now your meetings should focus on critically discussing your work. Let them advise you on the process of submission and learn from their experience.

It is vital at this stage that you revisit the PhD regulations, particularly those on submitting your thesis. Remember that the guidance may have been updated since you first started your PhD.

Codes and regulations for research students

Writing up qualitative research

This independent self study pack is aimed at Postgraduate Researchers working on a qualitative thesis who have completed their data collection and analysis and are at the stage of writing up.

Note: this self-study pack was written in 2013 so is not an expecially up-to-date resource, but it may still contain helpful general information.

The units available for download are:

Writing up: course introduction (PDF - 3 pages)

Unit 1: structure and introduction (PDF - 13 pages)

Unit 2: literature review (PDF - 15 pages)

Unit 3: methodology (PDF - 9 pages)

Unit 4: data chapters (PDF - 17 pages)

Unit 5: the final chapter (PDF - 19 pages)

Unit 6: the first few pages (PDF - 9 pages)

Independent study notes (PDF - 11 pages)

Preparing for the Viva

A Guide for Viva Preparation (PDF)

Preparing for an Online Viva (PDF)

It may be particularly important now that you get advice and support on your next career steps. Read out career management section for some timely advice, and an overview of support you can access from the University’s careers service.

Career management advice for PhD students

Training courses

To help you in the final stages of your research programme, we recommend attending some of the following  IAD  courses:

• 7 Reasons you'll Pass your Viva
• Thesis Workshops - School Specific
• Viva Survivor

Doing a skills audit to help plan your development

Revisit your skills audit and update it, you will have learnt a lot in during your PhD, and the chances are your development needs have now changed. Your focus for future development should now be on the skills you need to move forward into your career. Think about these carefully and if you want to, seek advice from the Careers Service.

Get help from the University Careers Service

If you haven’t done a skills audit before, doing an audit (i.e. an assessment) of your skills is useful; if you can identify what skills are important to your research success, and whether you are strong or weak in these areas. You can then focus your precious time on developing the areas that will help you most.

Other sources of support

Vitae: The Vitae resources on writing up, submitting and defending your thesis are particularly helpful at this stage.

Vitae guidance on completing your doctorate

This article was published on 2024-02-26

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Thesis and Dissertation: Getting Started

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The resources in this section are designed to provide guidance for the first steps of the thesis or dissertation writing process. They offer tools to support the planning and managing of your project, including writing out your weekly schedule, outlining your goals, and organzing the various working elements of your project.

Weekly Goals Sheet (a.k.a. Life Map) [Word Doc]

This editable handout provides a place for you to fill in available time blocks on a weekly chart that will help you visualize the amount of time you have available to write. By using this chart, you will be able to work your writing goals into your schedule and put these goals into perspective with your day-to-day plans and responsibilities each week. This handout also contains a formula to help you determine the minimum number of pages you would need to write per day in order to complete your writing on time.

Setting a Production Schedule (Word Doc)

This editable handout can help you make sense of the various steps involved in the production of your thesis or dissertation and determine how long each step might take. A large part of this process involves (1) seeking out the most accurate and up-to-date information regarding specific document formatting requirements, (2) understanding research protocol limitations, (3) making note of deadlines, and (4) understanding your personal writing habits.

Creating a Roadmap (PDF)

Part of organizing your writing involves having a clear sense of how the different working parts relate to one another. Creating a roadmap for your dissertation early on can help you determine what the final document will include and how all the pieces are connected. This resource offers guidance on several approaches to creating a roadmap, including creating lists, maps, nut-shells, visuals, and different methods for outlining. It is important to remember that you can create more than one roadmap (or more than one type of roadmap) depending on how the different approaches discussed here meet your needs.

Frequently asked questions

How long does it take to write a dissertation.

At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.

A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.

Frequently asked questions: Knowledge Base

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.

Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.

A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.

The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.

In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘ et al. ’

A bibliography should always contain every source you cited in your text. Sometimes a bibliography also contains other sources that you used in your research, but did not cite in the text.

MHRA doesn’t specify a rule about this, so check with your supervisor to find out exactly what should be included in your bibliography.

Footnote numbers should appear in superscript (e.g. 11 ). You can use the ‘Insert footnote’ button in Word to do this automatically; it’s in the ‘References’ tab at the top.

Footnotes always appear after the quote or paraphrase they relate to. MHRA generally recommends placing footnote numbers at the end of the sentence, immediately after any closing punctuation, like this. 12

In situations where this might be awkward or misleading, such as a long sentence containing multiple quotations, footnotes can also be placed at the end of a clause mid-sentence, like this; 13 note that they still come after any punctuation.

When a source has two or three authors, name all of them in your MHRA references . When there are four or more, use only the first name, followed by ‘and others’:

Note that in the bibliography, only the author listed first has their name inverted. The names of additional authors and those of translators or editors are written normally.

A citation should appear wherever you use information or ideas from a source, whether by quoting or paraphrasing its content.

In Vancouver style , you have some flexibility about where the citation number appears in the sentence – usually directly after mentioning the author’s name is best, but simply placing it at the end of the sentence is an acceptable alternative, as long as it’s clear what it relates to.

In Vancouver style , when you refer to a source with multiple authors in your text, you should only name the first author followed by ‘et al.’. This applies even when there are only two authors.

In your reference list, include up to six authors. For sources with seven or more authors, list the first six followed by ‘et al.’.

The words ‘ dissertation ’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:

• In the UK, you write a dissertation at the end of a bachelor’s or master’s degree, and you write a thesis to complete a PhD.
• In the US, it’s the other way around: you may write a thesis at the end of a bachelor’s or master’s degree, and you write a dissertation to complete a PhD.

The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.

Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic , and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).

Dissertation word counts vary widely across different fields, institutions, and levels of education:

• An undergraduate dissertation is typically 8,000–15,000 words
• A master’s dissertation is typically 12,000–50,000 words
• A PhD thesis is typically book-length: 70,000–100,000 words

However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.

References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.

If you don’t acknowledge your sources, you can get in trouble for plagiarism .

Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:

• Harvard referencing , the most commonly used style in UK universities.
• MHRA , used in humanities subjects.
• APA , used in the social sciences.
• Vancouver , used in biomedicine.
• OSCOLA , used in law.

Your university may have its own referencing style guide.

If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.

To avoid plagiarism , always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.

You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.

In Harvard style , when you quote directly from a source that includes page numbers, your in-text citation must include a page number. For example: (Smith, 2014, p. 33).

You can also include page numbers to point the reader towards a passage that you paraphrased . If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.

When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation , first mention the source you want to refer to, and then the source in which you found it. For example:

It’s advisable to avoid indirect citations wherever possible, because they suggest you don’t have full knowledge of the sources you’re citing. Only use an indirect citation if you can’t reasonably gain access to the original source.

In Harvard style referencing , to distinguish between two sources by the same author that were published in the same year, you add a different letter after the year for each source:

• (Smith, 2019a)
• (Smith, 2019b)

Add ‘a’ to the first one you cite, ‘b’ to the second, and so on. Do the same in your bibliography or reference list .

To create a hanging indent for your bibliography or reference list :

• Highlight all the entries
• Click on the arrow in the bottom-right corner of the ‘Paragraph’ tab in the top menu.
• In the pop-up window, under ‘Special’ in the ‘Indentation’ section, use the drop-down menu to select ‘Hanging’.
• Then close the window with ‘OK’.

Though the terms are sometimes used interchangeably, there is a difference in meaning:

• A reference list only includes sources cited in the text – every entry corresponds to an in-text citation .
• A bibliography also includes other sources which were consulted during the research but not cited.

It’s important to assess the reliability of information found online. Look for sources from established publications and institutions with expertise (e.g. peer-reviewed journals and government agencies).

The CRAAP test (currency, relevance, authority, accuracy, purpose) can aid you in assessing sources, as can our list of credible sources . You should generally avoid citing websites like Wikipedia that can be edited by anyone – instead, look for the original source of the information in the “References” section.

You can generally omit page numbers in your in-text citations of online sources which don’t have them. But when you quote or paraphrase a specific passage from a particularly long online source, it’s useful to find an alternate location marker.

For text-based sources, you can use paragraph numbers (e.g. ‘para. 4’) or headings (e.g. ‘under “Methodology”’). With video or audio sources, use a timestamp (e.g. ‘10:15’).

In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.

Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.

Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation .

Even if you feel your supervisor did not contribute greatly to the final product, you still should acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.

The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract .

In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.

You may acknowledge God in your thesis or dissertation acknowledgements , but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract   in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction.

An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).

Your dissertation sometimes contains a list of abbreviations .

As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.

Be sure to add each abbreviation in your list of abbreviations !

If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.

A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents . It should always be in alphabetical order.

Fishbone diagrams have a few different names that are used interchangeably, including herringbone diagram, cause-and-effect diagram, and Ishikawa diagram.

These are all ways to refer to the same thing– a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot solutions.

Fishbone diagrams (also called herringbone diagrams, cause-and-effect diagrams, and Ishikawa diagrams) are most popular in fields of quality management. They are also commonly used in nursing and healthcare, or as a brainstorming technique for students.

Some synonyms and near synonyms of among include:

• In the company of
• In the middle of
• Surrounded by

Some synonyms and near synonyms of between  include:

• In the space separating
• In the time separating

In spite of   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s always used in a subordinate clause to contrast with the information given in the main clause of a sentence (e.g., ‘Amy continued to watch TV, in spite of the time’).

Despite   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s used in a subordinate clause to contrast with information given in the main clause of a sentence (e.g., ‘Despite the stress, Joe loves his job’).

‘Log in’ is a phrasal verb meaning ‘connect to an electronic device, system, or app’. The preposition ‘to’ is often used directly after the verb; ‘in’ and ‘to’ should be written as two separate words (e.g., ‘ log in to the app to update privacy settings’).

‘Log into’ is sometimes used instead of ‘log in to’, but this is generally considered incorrect (as is ‘login to’).

Some synonyms and near synonyms of ensure include:

• Make certain

Some synonyms and near synonyms of assure  include:

Rest assured is an expression meaning ‘you can be certain’ (e.g., ‘Rest assured, I will find your cat’). ‘Assured’ is the adjectival form of the verb assure , meaning ‘convince’ or ‘persuade’.

Some synonyms and near synonyms for council include:

There are numerous synonyms and near synonyms for the two meanings of counsel :

AI writing tools can be used to perform a variety of tasks.

Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.

These tools can also be used to paraphrase or summarise text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarising tool , and grammar checker , which are designed specifically for these purposes.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalisation, unless it is allowed by your university. Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

The Scribbr Knowledge Base is a collection of free resources to help you succeed in academic research, writing, and citation. Every week, we publish helpful step-by-step guides, clear examples, simple templates, engaging videos, and more.

The Knowledge Base is for students at all levels. Whether you’re writing your first essay, working on your bachelor’s or master’s dissertation, or getting to grips with your PhD research, we’ve got you covered.

As well as the Knowledge Base, Scribbr provides many other tools and services to support you in academic writing and citation:

• Create your citations and manage your reference list with our free Reference Generators in APA and MLA style.
• Scan your paper for in-text citation errors and inconsistencies with our innovative APA Citation Checker .
• Avoid accidental plagiarism with our reliable Plagiarism Checker .
• Polish your writing and get feedback on structure and clarity with our Proofreading & Editing services .

Yes! We’re happy for educators to use our content, and we’ve even adapted some of our articles into ready-made lecture slides .

You are free to display, distribute, and adapt Scribbr materials in your classes or upload them in private learning environments like Blackboard. We only ask that you credit Scribbr for any content you use.

We’re always striving to improve the Knowledge Base. If you have an idea for a topic we should cover, or you notice a mistake in any of our articles, let us know by emailing [email protected] .

The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole paper by someone else will have the most severe consequences, while accidental citation errors are considered less serious.

If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offence or you’ve done it before.

As an academic or professional, plagiarising seriously damages your reputation. You might also lose your research funding or your job, and you could even face legal consequences for copyright infringement.

Paraphrasing without crediting the original author is a form of plagiarism , because you’re presenting someone else’s ideas as if they were your own.

However, paraphrasing is not plagiarism if you correctly reference the source . This means including an in-text referencing and a full reference , formatted according to your required citation style (e.g., Harvard , Vancouver ).

As well as referencing your source, make sure that any paraphrased text is completely rewritten in your own words.

Accidental plagiarism is one of the most common examples of plagiarism . Perhaps you forgot to cite a source, or paraphrased something a bit too closely. Maybe you can’t remember where you got an idea from, and aren’t totally sure if it’s original or not.

These all count as plagiarism, even though you didn’t do it on purpose. When in doubt, make sure you’re citing your sources . Also consider running your work through a plagiarism checker tool prior to submission, which work by using advanced database software to scan for matches between your text and existing texts.

Scribbr’s Plagiarism Checker takes less than 10 minutes and can help you turn in your paper with confidence.

The accuracy depends on the plagiarism checker you use. Per our in-depth research , Scribbr is the most accurate plagiarism checker. Many free plagiarism checkers fail to detect all plagiarism or falsely flag text as plagiarism.

Plagiarism checkers work by using advanced database software to scan for matches between your text and existing texts. Their accuracy is determined by two factors: the algorithm (which recognises the plagiarism) and the size of the database (with which your document is compared).

To avoid plagiarism when summarising an article or other source, follow these two rules:

• Write the summary entirely in your own words by   paraphrasing the author’s ideas.
• Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

Plagiarism can be detected by your professor or readers if the tone, formatting, or style of your text is different in different parts of your paper, or if they’re familiar with the plagiarised source.

Many universities also use   plagiarism detection software like Turnitin’s, which compares your text to a large database of other sources, flagging any similarities that come up.

It can be easier than you think to commit plagiarism by accident. Consider using a   plagiarism checker prior to submitting your essay to ensure you haven’t missed any citations.

Some examples of plagiarism include:

• Copying and pasting a Wikipedia article into the body of an assignment
• Quoting a source without including a citation
• Not paraphrasing a source properly (e.g. maintaining wording too close to the original)
• Forgetting to cite the source of an idea

The most surefire way to   avoid plagiarism is to always cite your sources . When in doubt, cite!

Global plagiarism means taking an entire work written by someone else and passing it off as your own. This can include getting someone else to write an essay or assignment for you, or submitting a text you found online as your own work.

Global plagiarism is one of the most serious types of plagiarism because it involves deliberately and directly lying about the authorship of a work. It can have severe consequences for students and professionals alike.

Verbatim plagiarism means copying text from a source and pasting it directly into your own document without giving proper credit.

If the structure and the majority of the words are the same as in the original source, then you are committing verbatim plagiarism. This is the case even if you delete a few words or replace them with synonyms.

If you want to use an author’s exact words, you need to quote the original source by putting the copied text in quotation marks and including an   in-text citation .

Patchwork plagiarism , also called mosaic plagiarism, means copying phrases, passages, or ideas from various existing sources and combining them to create a new text. This includes slightly rephrasing some of the content, while keeping many of the same words and the same structure as the original.

While this type of plagiarism is more insidious than simply copying and pasting directly from a source, plagiarism checkers like Turnitin’s can still easily detect it.

To avoid plagiarism in any form, remember to reference your sources .

Yes, reusing your own work without citation is considered self-plagiarism . This can range from resubmitting an entire assignment to reusing passages or data from something you’ve handed in previously.

Self-plagiarism often has the same consequences as other types of plagiarism . If you want to reuse content you wrote in the past, make sure to check your university’s policy or consult your professor.

If you are reusing content or data you used in a previous assignment, make sure to cite yourself. You can cite yourself the same way you would cite any other source: simply follow the directions for the citation style you are using.

Keep in mind that reusing prior content can be considered self-plagiarism , so make sure you ask your instructor or consult your university’s handbook prior to doing so.

Most institutions have an internal database of previously submitted student assignments. Turnitin can check for self-plagiarism by comparing your paper against this database. If you’ve reused parts of an assignment you already submitted, it will flag any similarities as potential plagiarism.

Online plagiarism checkers don’t have access to your institution’s database, so they can’t detect self-plagiarism of unpublished work. If you’re worried about accidentally self-plagiarising, you can use Scribbr’s Self-Plagiarism Checker to upload your unpublished documents and check them for similarities.

Plagiarism has serious consequences and can be illegal in certain scenarios.

While most of the time plagiarism in an undergraduate setting is not illegal, plagiarism or self-plagiarism in a professional academic setting can lead to legal action, including copyright infringement and fraud. Many scholarly journals do not allow you to submit the same work to more than one journal, and if you do not credit a coauthor, you could be legally defrauding them.

Even if you aren’t breaking the law, plagiarism can seriously impact your academic career. While the exact consequences of plagiarism vary by institution and severity, common consequences include a lower grade, automatically failing a course, academic suspension or probation, and even expulsion.

Self-plagiarism means recycling work that you’ve previously published or submitted as an assignment. It’s considered academic dishonesty to present something as brand new when you’ve already gotten credit and perhaps feedback for it in the past.

If you want to refer to ideas or data from previous work, be sure to cite yourself.

Academic integrity means being honest, ethical, and thorough in your academic work. To maintain academic integrity, you should avoid misleading your readers about any part of your research and refrain from offences like plagiarism and contract cheating, which are examples of academic misconduct.

Academic dishonesty refers to deceitful or misleading behavior in an academic setting. Academic dishonesty can occur intentionally or unintentionally, and it varies in severity.

It can encompass paying for a pre-written essay, cheating on an exam, or committing plagiarism . It can also include helping others cheat, copying a friend’s homework answers, or even pretending to be sick to miss an exam.

Academic dishonesty doesn’t just occur in a classroom setting, but also in research and other academic-adjacent fields.

Consequences of academic dishonesty depend on the severity of the offence and your institution’s policy. They can range from a warning for a first offence to a failing grade in a course to expulsion from your university.

For those in certain fields, such as nursing, engineering, or lab sciences, not learning fundamentals properly can directly impact the health and safety of others. For those working in academia or research, academic dishonesty impacts your professional reputation, leading others to doubt your future work.

Academic dishonesty can be intentional or unintentional, ranging from something as simple as claiming to have read something you didn’t to copying your neighbour’s answers on an exam.

You can commit academic dishonesty with the best of intentions, such as helping a friend cheat on a paper. Severe academic dishonesty can include buying a pre-written essay or the answers to a multiple-choice test, or falsifying a medical emergency to avoid taking a final exam.

Plagiarism means presenting someone else’s work as your own without giving proper credit to the original author. In academic writing, plagiarism involves using words, ideas, or information from a source without including a citation .

Plagiarism can have serious consequences , even when it’s done accidentally. To avoid plagiarism, it’s important to keep track of your sources and cite them correctly.

Common knowledge does not need to be cited. However, you should be extra careful when deciding what counts as common knowledge.

Common knowledge encompasses information that the average educated reader would accept as true without needing the extra validation of a source or citation.

Common knowledge should be widely known, undisputed, and easily verified. When in doubt, always cite your sources.

Most online plagiarism checkers only have access to public databases, whose software doesn’t allow you to compare two documents for plagiarism.

However, in addition to our Plagiarism Checker , Scribbr also offers an Self-Plagiarism Checker . This is an add-on tool that lets you compare your paper with unpublished or private documents. This way you can rest assured that you haven’t unintentionally plagiarised or self-plagiarised .

Compare two sources for plagiarism

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

The Scribbr Reference Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Reference Generator in our publicly accessible repository on Github .

To paraphrase effectively, don’t just take the original sentence and swap out some of the words for synonyms. Instead, try:

• Reformulating the sentence (e.g., change active to passive , or start from a different point)
• Combining information from multiple sentences into one
• Leaving out information from the original that isn’t relevant to your point
• Using synonyms where they don’t distort the meaning

The main point is to ensure you don’t just copy the structure of the original text, but instead reformulate the idea in your own words.

Plagiarism means using someone else’s words or ideas and passing them off as your own. Paraphrasing means putting someone else’s ideas into your own words.

So when does paraphrasing count as plagiarism?

• Paraphrasing is plagiarism if you don’t properly credit the original author.
• Paraphrasing is plagiarism if your text is too close to the original wording (even if you cite the source). If you directly copy a sentence or phrase, you should quote it instead.
• Paraphrasing  is not plagiarism if you put the author’s ideas completely into your own words and properly reference the source .

To present information from other sources in academic writing , it’s best to paraphrase in most cases. This shows that you’ve understood the ideas you’re discussing and incorporates them into your text smoothly.

It’s appropriate to quote when:

• Changing the phrasing would distort the meaning of the original text
• You want to discuss the author’s language choices (e.g., in literary analysis )
• You’re presenting a precise definition
• You’re looking in depth at a specific claim

A quote is an exact copy of someone else’s words, usually enclosed in quotation marks and credited to the original author or speaker.

Every time you quote a source , you must include a correctly formatted in-text citation . This looks slightly different depending on the citation style .

For example, a direct quote in APA is cited like this: ‘This is a quote’ (Streefkerk, 2020, p. 5).

Every in-text citation should also correspond to a full reference at the end of your paper.

In scientific subjects, the information itself is more important than how it was expressed, so quoting should generally be kept to a minimum. In the arts and humanities, however, well-chosen quotes are often essential to a good paper.

In social sciences, it varies. If your research is mainly quantitative , you won’t include many quotes, but if it’s more qualitative , you may need to quote from the data you collected .

As a general guideline, quotes should take up no more than 5–10% of your paper. If in doubt, check with your instructor or supervisor how much quoting is appropriate in your field.

If you’re quoting from a text that paraphrases or summarises other sources and cites them in parentheses , APA  recommends retaining the citations as part of the quote:

• Smith states that ‘the literature on this topic (Jones, 2015; Sill, 2019; Paulson, 2020) shows no clear consensus’ (Smith, 2019, p. 4).

Footnote or endnote numbers that appear within quoted text should be omitted.

If you want to cite an indirect source (one you’ve only seen quoted in another source), either locate the original source or use the phrase ‘as cited in’ in your citation.

A block quote is a long quote formatted as a separate ‘block’ of text. Instead of using quotation marks , you place the quote on a new line, and indent the entire quote to mark it apart from your own words.

APA uses block quotes for quotes that are 40 words or longer.

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

Common examples of primary sources include interview transcripts , photographs, novels, paintings, films, historical documents, and official statistics.

Anything you directly analyze or use as first-hand evidence can be a primary source, including qualitative or quantitative data that you collected yourself.

Common examples of secondary sources include academic books, journal articles , reviews, essays , and textbooks.

Anything that summarizes, evaluates or interprets primary sources can be a secondary source. If a source gives you an overview of background information or presents another researcher’s ideas on your topic, it is probably a secondary source.

To determine if a source is primary or secondary, ask yourself:

• Was the source created by someone directly involved in the events you’re studying (primary), or by another researcher (secondary)?
• Does the source provide original information (primary), or does it summarize information from other sources (secondary)?
• Are you directly analyzing the source itself (primary), or only using it for background information (secondary)?

Some types of sources are nearly always primary: works of art and literature, raw statistical data, official documents and records, and personal communications (e.g. letters, interviews ). If you use one of these in your research, it is probably a primary source.

Primary sources are often considered the most credible in terms of providing evidence for your argument, as they give you direct evidence of what you are researching. However, it’s up to you to ensure the information they provide is reliable and accurate.

Always make sure to properly cite your sources to avoid plagiarism .

A fictional movie is usually a primary source. A documentary can be either primary or secondary depending on the context.

If you are directly analysing some aspect of the movie itself – for example, the cinematography, narrative techniques, or social context – the movie is a primary source.

If you use the movie for background information or analysis about your topic – for example, to learn about a historical event or a scientific discovery – the movie is a secondary source.

Whether it’s primary or secondary, always properly cite the movie in the citation style you are using. Learn how to create an MLA movie citation or an APA movie citation .

Articles in newspapers and magazines can be primary or secondary depending on the focus of your research.

In historical studies, old articles are used as primary sources that give direct evidence about the time period. In social and communication studies, articles are used as primary sources to analyse language and social relations (for example, by conducting content analysis or discourse analysis ).

If you are not analysing the article itself, but only using it for background information or facts about your topic, then the article is a secondary source.

In academic writing , there are three main situations where quoting is the best choice:

• To analyse the author’s language (e.g., in a literary analysis essay )
• To give evidence from primary sources
• To accurately present a precise definition or argument

Don’t overuse quotes; your own voice should be dominant. If you just want to provide information from a source, it’s usually better to paraphrase or summarise .

Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.

Lists of figures and tables are often not required, and they aren’t particularly common. They specifically aren’t required for APA Style, though you should be careful to follow their other guidelines for figures and tables .

If you have many figures and tables in your thesis or dissertation, include one may help you stay organised. Your educational institution may require them, so be sure to check their guidelines.

Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article , look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.

If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.

A list of figures and tables compiles all of the figures and tables that you used in your thesis or dissertation and displays them with the page number where they can be found.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .

A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.

Definitional terms often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.

However, if you’d prefer to cite your sources , you can follow guidance for citing dictionary entries in MLA or APA style for your glossary.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organised by page number.

Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.

The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

Usually, no title page is needed in an MLA paper . A header is generally included at the top of the first page instead. The exceptions are when:

• Your instructor requires one, or
• Your paper is a group project

In those cases, you should use a title page instead of a header, listing the same information but on a separate page.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organise your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation, such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarises the contents of your paper.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper.

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page, after the title page and acknowledgements but before the table of contents .

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

A noun is a word that represents a person, thing, concept, or place (e.g., ‘John’, ‘house’, ‘affinity’, ‘river’). Most sentences contain at least one noun or pronoun .

Nouns are often, but not always, preceded by an article (‘the’, ‘a’, or ‘an’) and/or another determiner such as an adjective.

There are many ways to categorize nouns into various types, and the same noun can fall into multiple categories or even change types depending on context.

Some of the main types of nouns are:

• Common nouns and proper nouns
• Countable and uncountable nouns
• Concrete and abstract nouns
• Collective nouns
• Possessive nouns
• Attributive nouns
• Appositive nouns
• Generic nouns

Pronouns are words like ‘I’, ‘she’, and ‘they’ that are used in a similar way to nouns . They stand in for a noun that has already been mentioned or refer to yourself and other people.

Pronouns can function just like nouns as the head of a noun phrase and as the subject or object of a verb. However, pronouns change their forms (e.g., from ‘I’ to ‘me’) depending on the grammatical context they’re used in, whereas nouns usually don’t.

Common nouns are words for types of things, people, and places, such as ‘dog’, ‘professor’, and ‘city’. They are not capitalised and are typically used in combination with articles and other determiners.

Proper nouns are words for specific things, people, and places, such as ‘Max’, ‘Dr Prakash’, and ‘London’. They are always capitalised and usually aren’t combined with articles and other determiners.

A proper adjective is an adjective that was derived from a proper noun and is therefore capitalised .

Proper adjectives include words for nationalities, languages, and ethnicities (e.g., ‘Japanese’, ‘Inuit’, ‘French’) and words derived from people’s names (e.g., ‘Bayesian’, ‘Orwellian’).

The names of seasons (e.g., ‘spring’) are treated as common nouns in English and therefore not capitalised . People often assume they are proper nouns, but this is an error.

The names of days and months, however, are capitalised since they’re treated as proper nouns in English (e.g., ‘Wednesday’, ‘January’).

No, as a general rule, academic concepts, disciplines, theories, models, etc. are treated as common nouns , not proper nouns , and therefore not capitalised . For example, ‘five-factor model of personality’ or ‘analytic philosophy’.

However, proper nouns that appear within the name of an academic concept (such as the name of the inventor) are capitalised as usual. For example, ‘Darwin’s theory of evolution’ or ‘ Student’s t table ‘.

Collective nouns are most commonly treated as singular (e.g., ‘the herd is grazing’), but usage differs between US and UK English :

• In US English, it’s standard to treat all collective nouns as singular, even when they are plural in appearance (e.g., ‘The Rolling Stones is …’). Using the plural form is usually seen as incorrect.
• In UK English, collective nouns can be treated as singular or plural depending on context. It’s quite common to use the plural form, especially when the noun looks plural (e.g., ‘The Rolling Stones are …’).

The plural of “crisis” is “crises”. It’s a loanword from Latin and retains its original Latin plural noun form (similar to “analyses” and “bases”). It’s wrong to write “crisises”.

For example, you might write “Several crises destabilized the regime.”

Normally, the plural of “fish” is the same as the singular: “fish”. It’s one of a group of irregular plural nouns in English that are identical to the corresponding singular nouns (e.g., “moose”, “sheep”). For example, you might write “The fish scatter as the shark approaches.”

If you’re referring to several species of fish, though, the regular plural “fishes” is often used instead. For example, “The aquarium contains many different fishes , including trout and carp.”

The correct plural of “octopus” is “octopuses”.

People often write “octopi” instead because they assume that the plural noun is formed in the same way as Latin loanwords such as “fungus/fungi”. But “octopus” actually comes from Greek, where its original plural is “octopodes”. In English, it instead has the regular plural form “octopuses”.

For example, you might write “There are four octopuses in the aquarium.”

The plural of “moose” is the same as the singular: “moose”. It’s one of a group of plural nouns in English that are identical to the corresponding singular nouns. So it’s wrong to write “mooses”.

For example, you might write “There are several moose in the forest.”

Bias in research affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behaviour and external factors (difficult circumstances) to justify the same behaviour in themselves.

Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews . These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.

Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen either because people are not willing or not able to participate.

In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.

Demand characteristics are common problems in psychology experiments and other social science studies because they can bias your research findings.

Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.

These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity . You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.

You can control demand characteristics by taking a few precautions in your research design and materials.

Use these measures:

• Deception: Hide the purpose of the study from participants
• Between-groups design : Give each participant only one independent variable treatment
• Double-blind design : Conceal the assignment of groups from participants and yourself
• Implicit measures: Use indirect or hidden measurements for your variables

Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic research bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity .

To avoid attrition bias , applying some of these measures can help you reduce participant dropout (attrition) by making it easy and appealing for participants to stay.

• Provide compensation (e.g., cash or gift cards) for attending every session
• Minimise the number of follow-ups as much as possible
• Make all follow-ups brief, flexible, and convenient for participants
• Send participants routine reminders to schedule follow-ups
• Recruit more participants than you need for your sample (oversample)
• Maintain detailed contact information so you can get in touch with participants even if they move

If you have a small amount of attrition bias , you can use a few statistical methods to try to make up for this research bias .

Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.

Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.

The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.

Although there is no definite answer to what causes the placebo effect , researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.

Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.

Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.

Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.

Positivity bias is phenomenon that occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias , or the e xpectation that things will work out well, even if rationality suggests that problems are inevitable in life.

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

There are many ways to categorize adjectives into various types. An adjective can fall into one or more of these categories depending on how it is used.

Some of the main types of adjectives are:

• Attributive adjectives
• Predicative adjectives
• Comparative adjectives
• Superlative adjectives
• Coordinate adjectives
• Appositive adjectives
• Compound adjectives
• Participial adjectives
• Proper adjectives
• Denominal adjectives
• Nominal adjectives

Cardinal numbers (e.g., one, two, three) can be placed before a noun to indicate quantity (e.g., one apple). While these are sometimes referred to as ‘numeral adjectives ‘, they are more accurately categorised as determiners or quantifiers.

Proper adjectives are adjectives formed from a proper noun (i.e., the name of a specific person, place, or thing) that are used to indicate origin. Like proper nouns, proper adjectives are always capitalised (e.g., Newtonian, Marxian, African).

The cost of proofreading depends on the type and length of text, the turnaround time, and the level of services required. Most proofreading companies charge per word or page, while freelancers sometimes charge an hourly rate.

For proofreading alone, which involves only basic corrections of typos and formatting mistakes, you might pay as little as £0.01 per word, but in many cases, your text will also require some level of editing , which costs slightly more.

It’s often possible to purchase combined proofreading and editing services and calculate the price in advance based on your requirements.

Then and than are two commonly confused words . In the context of ‘better than’, you use ‘than’ with an ‘a’.

• Julie is better than Jesse.
• I’d rather spend my time with you than with him.
• I understand Eoghan’s point of view better than Claudia’s.

Use to and used to are commonly confused words . In the case of ‘used to do’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to do laundry once a week.
• They used to do each other’s hair.
• We used to do the dishes every day .

There are numerous synonyms and near synonyms for the various meanings of “ favour ”:

There are numerous synonyms and near synonyms for the two meanings of “ favoured ”:

No one (two words) is an indefinite pronoun meaning ‘nobody’. People sometimes mistakenly write ‘noone’, but this is incorrect and should be avoided. ‘No-one’, with a hyphen, is also acceptable in UK English .

Nobody and no one are both indefinite pronouns meaning ‘no person’. They can be used interchangeably (e.g., ‘nobody is home’ means the same as ‘no one is home’).

Some synonyms and near synonyms of  every time include:

• Without exception

‘Everytime’ is sometimes used to mean ‘each time’ or ‘whenever’. However, this is incorrect and should be avoided. The correct phrase is every time   (two words).

Yes, the conjunction because is a compound word , but one with a long history. It originates in Middle English from the preposition “bi” (“by”) and the noun “cause”. Over time, the open compound “bi cause” became the closed compound “because”, which we use today.

Though it’s spelled this way now, the verb “be” is not one of the words that makes up “because”.

Yes, today is a compound word , but a very old one. It wasn’t originally formed from the preposition “to” and the noun “day”; rather, it originates from their Old English equivalents, “tō” and “dæġe”.

In the past, it was sometimes written as a hyphenated compound: “to-day”. But the hyphen is no longer included; it’s always “today” now (“to day” is also wrong).

IEEE citation format is defined by the Institute of Electrical and Electronics Engineers and used in their publications.

It’s also a widely used citation style for students in technical fields like electrical and electronic engineering, computer science, telecommunications, and computer engineering.

An IEEE in-text citation consists of a number in brackets at the relevant point in the text, which points the reader to the right entry in the numbered reference list at the end of the paper. For example, ‘Smith [1] states that …’

A location marker such as a page number is also included within the brackets when needed: ‘Smith [1, p. 13] argues …’

The IEEE reference page consists of a list of references numbered in the order they were cited in the text. The title ‘References’ appears in bold at the top, either left-aligned or centered.

The numbers appear in square brackets on the left-hand side of the page. The reference entries are indented consistently to separate them from the numbers. Entries are single-spaced, with a normal paragraph break between them.

If you cite the same source more than once in your writing, use the same number for all of the IEEE in-text citations for that source, and only include it on the IEEE reference page once. The source is numbered based on the first time you cite it.

For example, the fourth source you cite in your paper is numbered [4]. If you cite it again later, you still cite it as [4]. You can cite different parts of the source each time by adding page numbers [4, p. 15].

A verb is a word that indicates a physical action (e.g., ‘drive’), a mental action (e.g., ‘think’) or a state of being (e.g., ‘exist’). Every sentence contains a verb.

Verbs are almost always used along with a noun or pronoun to describe what the noun or pronoun is doing.

There are many ways to categorize verbs into various types. A verb can fall into one or more of these categories depending on how it is used.

Some of the main types of verbs are:

• Regular verbs
• Irregular verbs
• Transitive verbs
• Intransitive verbs
• Dynamic verbs
• Stative verbs
• Linking verbs
• Auxiliary verbs
• Modal verbs
• Phrasal verbs

Regular verbs are verbs whose simple past and past participle are formed by adding the suffix ‘-ed’ (e.g., ‘walked’).

Irregular verbs are verbs that form their simple past and past participles in some way other than by adding the suffix ‘-ed’ (e.g., ‘sat’).

The indefinite articles a and an are used to refer to a general or unspecified version of a noun (e.g., a house). Which indefinite article you use depends on the pronunciation of the word that follows it.

• A is used for words that begin with a consonant sound (e.g., a bear).
• An is used for words that begin with a vowel sound (e.g., an eagle).

Indefinite articles can only be used with singular countable nouns . Like definite articles, they are a type of determiner .

Editing and proofreading are different steps in the process of revising a text.

Editing comes first, and can involve major changes to content, structure and language. The first stages of editing are often done by authors themselves, while a professional editor makes the final improvements to grammar and style (for example, by improving sentence structure and word choice ).

Proofreading is the final stage of checking a text before it is published or shared. It focuses on correcting minor errors and inconsistencies (for example, in punctuation and capitalization ). Proofreaders often also check for formatting issues, especially in print publishing.

Whether you’re publishing a blog, submitting a research paper , or even just writing an important email, there are a few techniques you can use to make sure it’s error-free:

• Take a break : Set your work aside for at least a few hours so that you can look at it with fresh eyes.
• Proofread a printout : Staring at a screen for too long can cause fatigue – sit down with a pen and paper to check the final version.
• Use digital shortcuts : Take note of any recurring mistakes (for example, misspelling a particular word, switching between US and UK English , or inconsistently capitalizing a term), and use Find and Replace to fix it throughout the document.

If you want to be confident that an important text is error-free, it might be worth choosing a professional proofreading service instead.

There are many different routes to becoming a professional proofreader or editor. The necessary qualifications depend on the field – to be an academic or scientific proofreader, for example, you will need at least a university degree in a relevant subject.

For most proofreading jobs, experience and demonstrated skills are more important than specific qualifications. Often your skills will be tested as part of the application process.

To learn practical proofreading skills, you can choose to take a course with a professional organisation such as the Society for Editors and Proofreaders . Alternatively, you can apply to companies that offer specialised on-the-job training programmes, such as the Scribbr Academy .

Though they’re pronounced the same, there’s a big difference in meaning between its and it’s .

• ‘The cat ate its food’.
• ‘It’s almost Christmas’.

Its and it’s are often confused, but its (without apostrophe) is the possessive form of ‘it’ (e.g., its tail, its argument, its wing). You use ‘its’ instead of ‘his’ and ‘her’ for neuter, inanimate nouns.

Then and than are two commonly confused words with different meanings and grammatical roles.

• Then (pronounced with a short ‘e’ sound) refers to time. It’s often an adverb , but it can also be used as a noun meaning ‘that time’ and as an adjective referring to a previous status.
• Than (pronounced with a short ‘a’ sound) is used for comparisons. Grammatically, it usually functions as a conjunction , but sometimes it’s a preposition .

Use to and used to are commonly confused words . In the case of ‘used to be’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to be the new coworker.
• There used to be 4 cookies left.
• We used to walk to school every day .

A grammar checker is a tool designed to automatically check your text for spelling errors, grammatical issues, punctuation mistakes , and problems with sentence structure . You can check out our analysis of the best free grammar checkers to learn more.

A paraphrasing tool edits your text more actively, changing things whether they were grammatically incorrect or not. It can paraphrase your sentences to make them more concise and readable or for other purposes. You can check out our analysis of the best free paraphrasing tools to learn more.

Some tools available online combine both functions. Others, such as QuillBot , have separate grammar checker and paraphrasing tools. Be aware of what exactly the tool you’re using does to avoid introducing unwanted changes.

Good grammar is the key to expressing yourself clearly and fluently, especially in professional communication and academic writing . Word processors, browsers, and email programs typically have built-in grammar checkers, but they’re quite limited in the kinds of problems they can fix.

If you want to go beyond detecting basic spelling errors, there are many online grammar checkers with more advanced functionality. They can often detect issues with punctuation , word choice, and sentence structure that more basic tools would miss.

Not all of these tools are reliable, though. You can check out our research into the best free grammar checkers to explore the options.

Our research indicates that the best free grammar checker available online is the QuillBot grammar checker .

We tested 10 of the most popular checkers with the same sample text (containing 20 grammatical errors) and found that QuillBot easily outperformed the competition, scoring 18 out of 20, a drastic improvement over the second-place score of 13 out of 20.

It even appeared to outperform the premium versions of other grammar checkers, despite being entirely free.

A teacher’s aide is a person who assists in teaching classes but is not a qualified teacher. Aide is a noun meaning ‘assistant’, so it will always refer to a person.

‘Teacher’s aid’ is incorrect.

A visual aid is an instructional device (e.g., a photo, a chart) that appeals to vision to help you understand written or spoken information. Aid is often placed after an attributive noun or adjective (like ‘visual’) that describes the type of help provided.

‘Visual aide’ is incorrect.

A job aid is an instructional tool (e.g., a checklist, a cheat sheet) that helps you work efficiently. Aid is a noun meaning ‘assistance’. It’s often placed after an adjective or attributive noun (like ‘job’) that describes the specific type of help provided.

‘Job aide’ is incorrect.

There are numerous synonyms for the various meanings of truly :

Yours truly is a phrase used at the end of a formal letter or email. It can also be used (typically in a humorous way) as a pronoun to refer to oneself (e.g., ‘The dinner was cooked by yours truly ‘). The latter usage should be avoided in formal writing.

It’s formed by combining the second-person possessive pronoun ‘yours’ with the adverb ‘ truly ‘.

A pathetic fallacy can be a short phrase or a whole sentence and is often used in novels and poetry. Pathetic fallacies serve multiple purposes, such as:

• Conveying the emotional state of the characters or the narrator
• Creating an atmosphere or set the mood of a scene
• Foreshadowing events to come
• Giving texture and vividness to a piece of writing
• Communicating emotion to the reader in a subtle way, by describing the external world.
• Bringing inanimate objects to life so that they seem more relatable.

AMA citation format is a citation style designed by the American Medical Association. It’s frequently used in the field of medicine.

You may be told to use AMA style for your student papers. You will also have to follow this style if you’re submitting a paper to a journal published by the AMA.

An AMA in-text citation consists of the number of the relevant reference on your AMA reference page , written in superscript 1 at the point in the text where the source is used.

It may also include the page number or range of the relevant material in the source (e.g., the part you quoted 2(p46) ). Multiple sources can be cited at one point, presented as a range or list (with no spaces 3,5–9 ).

An AMA reference usually includes the author’s last name and initials, the title of the source, information about the publisher or the publication it’s contained in, and the publication date. The specific details included, and the formatting, depend on the source type.

References in AMA style are presented in numerical order (numbered by the order in which they were first cited in the text) on your reference page. A source that’s cited repeatedly in the text still only appears once on the reference page.

An AMA in-text citation just consists of the number of the relevant entry on your AMA reference page , written in superscript at the point in the text where the source is referred to.

You don’t need to mention the author of the source in your sentence, but you can do so if you want. It’s not an official part of the citation, but it can be useful as part of a signal phrase introducing the source.

On your AMA reference page , author names are written with the last name first, followed by the initial(s) of their first name and middle name if mentioned.

There’s a space between the last name and the initials, but no space or punctuation between the initials themselves. The names of multiple authors are separated by commas , and the whole list ends in a period, e.g., ‘Andreessen F, Smith PW, Gonzalez E’.

The names of up to six authors should be listed for each source on your AMA reference page , separated by commas . For a source with seven or more authors, you should list the first three followed by ‘ et al’ : ‘Isidore, Gilbert, Gunvor, et al’.

In the text, mentioning author names is optional (as they aren’t an official part of AMA in-text citations ). If you do mention them, though, you should use the first author’s name followed by ‘et al’ when there are three or more : ‘Isidore et al argue that …’

Note that according to AMA’s rather minimalistic punctuation guidelines, there’s no period after ‘et al’ unless it appears at the end of a sentence. This is different from most other styles, where there is normally a period.

Yes, you should normally include an access date in an AMA website citation (or when citing any source with a URL). This is because webpages can change their content over time, so it’s useful for the reader to know when you accessed the page.

When a publication or update date is provided on the page, you should include it in addition to the access date. The access date appears second in this case, e.g., ‘Published June 19, 2021. Accessed August 29, 2022.’

Don’t include an access date when citing a source with a DOI (such as in an AMA journal article citation ).

Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement . For example, income is a variable that can be recorded on an ordinal or a ratio scale:

• At an ordinal level , you could create 5 income groupings and code the incomes that fall within them from 1–5.
• At a ratio level , you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyse data in more ways. The higher the level of measurement, the more precise your data is.

The level at which you measure a variable determines how you can analyse your data.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis .

Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:

• Nominal : the data can only be categorised.
• Ordinal : the data can be categorised and ranked.
• Interval : the data can be categorised and ranked, and evenly spaced.
• Ratio : the data can be categorised, ranked, evenly spaced and has a natural zero.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

As the degrees of freedom increase, Student’s t distribution becomes less leptokurtic , meaning that the probability of extreme values decreases. The distribution becomes more and more similar to a standard normal distribution .

When there are only one or two degrees of freedom , the chi-square distribution is shaped like a backwards ‘J’. When there are three or more degrees of freedom, the distribution is shaped like a right-skewed hump. As the degrees of freedom increase, the hump becomes less right-skewed and the peak of the hump moves to the right. The distribution becomes more and more similar to a normal distribution .

‘Looking forward in hearing from you’ is an incorrect version of the phrase looking forward to hearing from you . The phrasal verb ‘looking forward to’ always needs the preposition ‘to’, not ‘in’.

• I am looking forward in hearing from you.
• I am looking forward to hearing from you.

Some synonyms and near synonyms for the expression looking forward to hearing from you include:

• Eagerly awaiting your response
• Hoping to hear from you soon
• It would be great to hear back from you
• Thanks in advance for your reply

People sometimes mistakenly write ‘looking forward to hear from you’, but this is incorrect. The correct phrase is looking forward to hearing from you .

The phrasal verb ‘look forward to’ is always followed by a direct object, the thing you’re looking forward to. As the direct object has to be a noun phrase , it should be the gerund ‘hearing’, not the verb ‘hear’.

• I’m looking forward to hear from you soon.
• I’m looking forward to hearing from you soon.

Traditionally, the sign-off Yours sincerely is used in an email message or letter when you are writing to someone you have interacted with before, not a complete stranger.

Yours faithfully is used instead when you are writing to someone you have had no previous correspondence with, especially if you greeted them as ‘ Dear Sir or Madam ’.

Just checking in   is a standard phrase used to start an email (or other message) that’s intended to ask someone for a response or follow-up action in a friendly, informal way. However, it’s a cliché opening that can come across as passive-aggressive, so we recommend avoiding it in favor of a more direct opening like “We previously discussed …”

In a more personal context, you might encounter “just checking in” as part of a longer phrase such as “I’m just checking in to see how you’re doing”. In this case, it’s not asking the other person to do anything but rather asking about their well-being (emotional or physical) in a friendly way.

“Earliest convenience” is part of the phrase at your earliest convenience , meaning “as soon as you can”. 

It’s typically used to end an email in a formal context by asking the recipient to do something when it’s convenient for them to do so.

ASAP is an abbreviation of the phrase “as soon as possible”. 

It’s typically used to indicate a sense of urgency in highly informal contexts (e.g., “Let me know ASAP if you need me to drive you to the airport”).

“ASAP” should be avoided in more formal correspondence. Instead, use an alternative like at your earliest convenience .

Some synonyms and near synonyms of the verb   compose   (meaning “to make up”) are:

People increasingly use “comprise” as a synonym of “compose.” However, this is normally still seen as a mistake, and we recommend avoiding it in your academic writing . “Comprise” traditionally means “to be made up of,” not “to make up.”

Some synonyms and near synonyms of the verb comprise are:

• Be composed of
• Be made up of

People increasingly use “comprise” interchangeably with “compose,” meaning that they consider words like “compose,” “constitute,” and “form” to be synonymous with “comprise.” However, this is still normally regarded as an error, and we advise against using these words interchangeably in academic writing .

A fallacy is a mistaken belief, particularly one based on unsound arguments or one that lacks the evidence to support it. Common types of fallacy that may compromise the quality of your research are:

• Correlation/causation fallacy: Claiming that two events that occur together have a cause-and-effect relationship even though this can’t be proven
• Ecological fallacy : Making inferences about the nature of individuals based on aggregate data for the group
• The sunk cost fallacy : Following through on a project or decision because we have already invested time, effort, or money into it, even if the current costs outweigh the benefits
• The base-rate fallacy : Ignoring base-rate or statistically significant information, such as sample size or the relative frequency of an event, in favor of  less relevant information e.g., pertaining to a single case, or a small number of cases
• The planning fallacy : Underestimating the time needed to complete a future task, even when we know that similar tasks in the past have taken longer than planned

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias or positivity bias.

Although both red herring fallacy and straw man fallacy are logical fallacies or reasoning errors, they denote different attempts to “win” an argument. More specifically:

• A red herring fallacy refers to an attempt to change the subject and divert attention from the original issue. In other words, a seemingly solid but ultimately irrelevant argument is introduced into the discussion, either on purpose or by mistake.
• A straw man argument involves the deliberate distortion of another person’s argument. By oversimplifying or exaggerating it, the other party creates an easy-to-refute argument and then attacks it.

The red herring fallacy is a problem because it is flawed reasoning. It is a distraction device that causes people to become sidetracked from the main issue and draw wrong conclusions.

Although a red herring may have some kernel of truth, it is used as a distraction to keep our eyes on a different matter. As a result, it can cause us to accept and spread misleading information.

The sunk cost fallacy and escalation of commitment (or commitment bias ) are two closely related terms. However, there is a slight difference between them:

• Escalation of commitment (aka commitment bias ) is the tendency to be consistent with what we have already done or said we will do in the past, especially if we did so in public. In other words, it is an attempt to save face and appear consistent.
• Sunk cost fallacy is the tendency to stick with a decision or a plan even when it’s failing. Because we have already invested valuable time, money, or energy, quitting feels like these resources were wasted.

In other words, escalating commitment is a manifestation of the sunk cost fallacy: an irrational escalation of commitment frequently occurs when people refuse to accept that the resources they’ve already invested cannot be recovered. Instead, they insist on more spending to justify the initial investment (and the incurred losses).

When you are faced with a straw man argument , the best way to respond is to draw attention to the fallacy and ask your discussion partner to show how your original statement and their distorted version are the same. Since these are different, your partner will either have to admit that their argument is invalid or try to justify it by using more flawed reasoning, which you can then attack.

The straw man argument is a problem because it occurs when we fail to take an opposing point of view seriously. Instead, we intentionally misrepresent our opponent’s ideas and avoid genuinely engaging with them. Due to this, resorting to straw man fallacy lowers the standard of constructive debate.

A straw man argument is a distorted (and weaker) version of another person’s argument that can easily be refuted (e.g., when a teacher proposes that the class spend more time on math exercises, a parent complains that the teacher doesn’t care about reading and writing).

This is a straw man argument because it misrepresents the teacher’s position, which didn’t mention anything about cutting down on reading and writing. The straw man argument is also known as the straw man fallacy .

A slippery slope argument is not always a fallacy.

• When someone claims adopting a certain policy or taking a certain action will automatically lead to a series of other policies or actions also being taken, this is a slippery slope argument.
• If they don’t show a causal connection between the advocated policy and the consequent policies, then they commit a slippery slope fallacy .

There are a number of ways you can deal with slippery slope arguments especially when you suspect these are fallacious:

• Slippery slope arguments take advantage of the gray area between an initial action or decision and the possible next steps that might lead to the undesirable outcome. You can point out these missing steps and ask your partner to indicate what evidence exists to support the claimed relationship between two or more events.
• Ask yourself if each link in the chain of events or action is valid. Every proposition has to be true for the overall argument to work, so even if one link is irrational or not supported by evidence, then the argument collapses.
• Sometimes people commit a slippery slope fallacy unintentionally. In these instances, use an example that demonstrates the problem with slippery slope arguments in general (e.g., by using statements to reach a conclusion that is not necessarily relevant to the initial statement). By attacking the concept of slippery slope arguments you can show that they are often fallacious.

People sometimes confuse cognitive bias and logical fallacies because they both relate to flawed thinking. However, they are not the same:

• Cognitive bias is the tendency to make decisions or take action in an illogical way because of our values, memory, socialization, and other personal attributes. In other words, it refers to a fixed pattern of thinking rooted in the way our brain works.
• Logical fallacies relate to how we make claims and construct our arguments in the moment. They are statements that sound convincing at first but can be disproven through logical reasoning.

In other words, cognitive bias refers to an ongoing predisposition, while logical fallacy refers to mistakes of reasoning that occur in the moment.

An appeal to ignorance (ignorance here meaning lack of evidence) is a type of informal logical fallacy .

It asserts that something must be true because it hasn’t been proven false—or that something must be false because it has not yet been proven true.

For example, “unicorns exist because there is no evidence that they don’t.” The appeal to ignorance is also called the burden of proof fallacy .

An ad hominem (Latin for “to the person”) is a type of informal logical fallacy . Instead of arguing against a person’s position, an ad hominem argument attacks the person’s character or actions in an effort to discredit them.

This rhetorical strategy is fallacious because a person’s character, motive, education, or other personal trait is logically irrelevant to whether their argument is true or false.

Name-calling is common in ad hominem fallacy (e.g., “environmental activists are ineffective because they’re all lazy tree-huggers”).

Ad hominem is a persuasive technique where someone tries to undermine the opponent’s argument by personally attacking them.

In this way, one can redirect the discussion away from the main topic and to the opponent’s personality without engaging with their viewpoint. When the opponent’s personality is irrelevant to the discussion, we call it an ad hominem fallacy .

Ad hominem tu quoque (‘you too”) is an attempt to rebut a claim by attacking its proponent on the grounds that they uphold a double standard or that they don’t practice what they preach. For example, someone is telling you that you should drive slowly otherwise you’ll get a speeding ticket one of these days, and you reply “but you used to get them all the time!”

Argumentum ad hominem means “argument to the person” in Latin and it is commonly referred to as ad hominem argument or personal attack. Ad hominem arguments are used in debates to refute an argument by attacking the character of the person making it, instead of the logic or premise of the argument itself.

The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence .

It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.

To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:

• In statistics , if we want to draw inferences about an entire population, we need to make sure that the sample is random and representative of the population . We can achieve that by using a probability sampling method , like simple random sampling or stratified sampling .
• In academic writing , use precise language and measured phases. Try to avoid making absolute claims, cite specific instances and examples without applying the findings to a larger group.
• As readers, we need to ask ourselves “does the writer demonstrate sufficient knowledge of the situation or phenomenon that would allow them to make a generalization?”

The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:

• The hasty generalization fallacy involves genuinely considering an example or case (i.e., the evidence comes first and then an incorrect conclusion is drawn from this).
• The anecdotal evidence fallacy (also known as “cherry-picking” ) is knowing in advance what conclusion we want to support, and then selecting the story (or a few stories) that support it. By overemphasizing anecdotal evidence that fits well with the point we are trying to make, we overlook evidence that would undermine our argument.

Although many sources use circular reasoning fallacy and begging the question interchangeably, others point out that there is a subtle difference between the two:

• Begging the question fallacy occurs when you assume that an argument is true in order to justify a conclusion. If something begs the question, what you are actually asking is, “Is the premise of that argument actually true?” For example, the statement “Snakes make great pets. That’s why we should get a snake” begs the question “are snakes really great pets?”
• Circular reasoning fallacy on the other hand, occurs when the evidence used to support a claim is just a repetition of the claim itself.  For example, “People have free will because they can choose what to do.”

In other words, we could say begging the question is a form of circular reasoning.

Circular reasoning fallacy uses circular reasoning to support an argument. More specifically, the evidence used to support a claim is just a repetition of the claim itself. For example: “The President of the United States is a good leader (claim), because they are the leader of this country (supporting evidence)”.

An example of a non sequitur is the following statement:

“Giving up nuclear weapons weakened the United States’ military. Giving up nuclear weapons also weakened China. For this reason, it is wrong to try to outlaw firearms in the United States today.”

Clearly there is a step missing in this line of reasoning and the conclusion does not follow from the premise, resulting in a non sequitur fallacy .

The difference between the post hoc fallacy and the non sequitur fallacy is that post hoc fallacy infers a causal connection between two events where none exists, whereas the non sequitur fallacy infers a conclusion that lacks a logical connection to the premise.

In other words, a post hoc fallacy occurs when there is a lack of a cause-and-effect relationship, while a non sequitur fallacy occurs when there is a lack of logical connection.

An example of post hoc fallacy is the following line of reasoning:

“Yesterday I had ice cream, and today I have a terrible stomachache. I’m sure the ice cream caused this.”

Although it is possible that the ice cream had something to do with the stomachache, there is no proof to justify the conclusion other than the order of events. Therefore, this line of reasoning is fallacious.

Post hoc fallacy and hasty generalisation fallacy are similar in that they both involve jumping to conclusions. However, there is a difference between the two:

• Post hoc fallacy is assuming a cause and effect relationship between two events, simply because one happened after the other.
• Hasty generalisation fallacy is drawing a general conclusion from a small sample or little evidence.

In other words, post hoc fallacy involves a leap to a causal claim; hasty generalisation fallacy involves a leap to a general proposition.

The fallacy of composition is similar to and can be confused with the hasty generalization fallacy . However, there is a difference between the two:

• The fallacy of composition involves drawing an inference about the characteristics of a whole or group based on the characteristics of its individual members.
• The hasty generalization fallacy involves drawing an inference about a population or class of things on the basis of few atypical instances or a small sample of that population or thing.

In other words, the fallacy of composition is using an unwarranted assumption that we can infer something about a whole based on the characteristics of its parts, while the hasty generalization fallacy is using insufficient evidence to draw a conclusion.

The opposite of the fallacy of composition is the fallacy of division . In the fallacy of division, the assumption is that a characteristic which applies to a whole or a group must necessarily apply to the parts or individual members. For example, “Australians travel a lot. Gary is Australian, so he must travel a lot.”

Base rate fallacy can be avoided by following these steps:

• Avoid making an important decision in haste. When we are under pressure, we are more likely to resort to cognitive shortcuts like the availability heuristic and the representativeness heuristic . Due to this, we are more likely to factor in only current and vivid information, and ignore the actual probability of something happening (i.e., base rate).
• Take a long-term view on the decision or question at hand. Look for relevant statistical data, which can reveal long-term trends and give you the full picture.
• Talk to experts like professionals. They are more aware of probabilities related to specific decisions.

Suppose there is a population consisting of 90% psychologists and 10% engineers. Given that you know someone enjoyed physics at school, you may conclude that they are an engineer rather than a psychologist, even though you know that this person comes from a population consisting of far more psychologists than engineers.

When we ignore the rate of occurrence of some trait in a population (the base-rate information) we commit base rate fallacy .

Cost-benefit fallacy is a common error that occurs when allocating sources in project management. It is the fallacy of assuming that cost-benefit estimates are more or less accurate, when in fact they are highly inaccurate and biased. This means that cost-benefit analyses can be useful, but only after the cost-benefit fallacy has been acknowledged and corrected for. Cost-benefit fallacy is a type of base rate fallacy .

In advertising, the fallacy of equivocation is often used to create a pun. For example, a billboard company might advertise their billboards using a line like: “Looking for a sign? This is it!” The word sign has a literal meaning as billboard and a figurative one as a sign from God, the universe, etc.

Equivocation is a fallacy because it is a form of argumentation that is both misleading and logically unsound. When the meaning of a word or phrase shifts in the course of an argument, it causes confusion and also implies that the conclusion (which may be true) does not follow from the premise.

The fallacy of equivocation is an informal logical fallacy, meaning that the error lies in the content of the argument instead of the structure.

Fallacies of relevance are a group of fallacies that occur in arguments when the premises are logically irrelevant to the conclusion. Although at first there seems to be a connection between the premise and the conclusion, in reality fallacies of relevance use unrelated forms of appeal.

For example, the genetic fallacy makes an appeal to the source or origin of the claim in an attempt to assert or refute something.

The ad hominem fallacy and the genetic fallacy are closely related in that they are both fallacies of relevance. In other words, they both involve arguments that use evidence or examples that are not logically related to the argument at hand. However, there is a difference between the two:

• In the ad hominem fallacy , the goal is to discredit the argument by discrediting the person currently making the argument.
• In the genetic fallacy , the goal is to discredit the argument by discrediting the history or origin (i.e., genesis) of an argument.

False dilemma fallacy is also known as false dichotomy, false binary, and “either-or” fallacy. It is the fallacy of presenting only two choices, outcomes, or sides to an argument as the only possibilities, when more are available.

The false dilemma fallacy works in two ways:

• By presenting only two options as if these were the only ones available
• By presenting two options as mutually exclusive (i.e., only one option can be selected or can be true at a time)

In both cases, by using the false dilemma fallacy, one conceals alternative choices and doesn’t allow others to consider the full range of options. This is usually achieved through an“either-or” construction and polarised, divisive language (“you are either a friend or an enemy”).

The best way to avoid a false dilemma fallacy is to pause and reflect on two points:

• Are the options presented truly the only ones available ? It could be that another option has been deliberately omitted.
• Are the options mentioned mutually exclusive ? Perhaps all of the available options can be selected (or be true) at the same time, which shows that they aren’t mutually exclusive. Proving this is called “escaping between the horns of the dilemma.”

Begging the question fallacy is an argument in which you assume what you are trying to prove. In other words, your position and the justification of that position are the same, only slightly rephrased.

For example: “All freshmen should attend college orientation, because all college students should go to such an orientation.”

The complex question fallacy and begging the question fallacy are similar in that they are both based on assumptions. However, there is a difference between them:

• A complex question fallacy occurs when someone asks a question that presupposes the answer to another question that has not been established or accepted by the other person. For example, asking someone “Have you stopped cheating on tests?”, unless it has previously been established that the person is indeed cheating on tests, is a fallacy.
• Begging the question fallacy occurs when we assume the very thing as a premise that we’re trying to prove in our conclusion. In other words, the conclusion is used to support the premises, and the premises prove the validity of the conclusion. For example: “God exists because the Bible says so, and the Bible is true because it is the word of God.”

In other words, begging the question is about drawing a conclusion based on an assumption, while a complex question involves asking a question that presupposes the answer to a prior question.

“ No true Scotsman ” arguments aren’t always fallacious. When there is a generally accepted definition of who or what constitutes a group, it’s reasonable to use statements in the form of “no true Scotsman”.

For example, the statement that “no true pacifist would volunteer for military service” is not fallacious, since a pacifist is, by definition, someone who opposes war or violence as a means of settling disputes.

No true Scotsman arguments are fallacious because instead of logically refuting the counterexample, they simply assert that it doesn’t count. In other words, the counterexample is rejected for psychological, but not logical, reasons.

The appeal to purity or no true Scotsman fallacy is an attempt to defend a generalisation about a group from a counterexample by shifting the definition of the group in the middle of the argument. In this way, one can exclude the counterexample as not being “true”, “genuine”, or “pure” enough to be considered as part of the group in question.

To identify an appeal to authority fallacy , you can ask yourself the following questions:

• Is the authority cited really a qualified expert in this particular area under discussion? For example, someone who has formal education or years of experience can be an expert.
• Do experts disagree on this particular subject? If that is the case, then for almost any claim supported by one expert there will be a counterclaim that is supported by another expert. If there is no consensus, an appeal to authority is fallacious.
• Is the authority in question biased? If you suspect that an expert’s prejudice and bias could have influenced their views, then the expert is not reliable and an argument citing this expert will be fallacious.To identify an appeal to authority fallacy, you ask yourself whether the authority cited is a qualified expert in the particular area under discussion.

Appeal to authority is a fallacy when those who use it do not provide any justification to support their argument. Instead they cite someone famous who agrees with their viewpoint, but is not qualified to make reliable claims on the subject.

Appeal to authority fallacy is often convincing because of the effect authority figures have on us. When someone cites a famous person, a well-known scientist, a politician, etc. people tend to be distracted and often fail to critically examine whether the authority figure is indeed an expert in the area under discussion.

The ad populum fallacy is common in politics. One example is the following viewpoint: “The majority of our countrymen think we should have military operations overseas; therefore, it’s the right thing to do.”

This line of reasoning is fallacious, because popular acceptance of a belief or position does not amount to a justification of that belief. In other words, following the prevailing opinion without examining the underlying reasons is irrational.

The ad populum fallacy plays on our innate desire to fit in (known as “bandwagon effect”). If many people believe something, our common sense tells us that it must be true and we tend to accept it. However, in logic, the popularity of a proposition cannot serve as evidence of its truthfulness.

Ad populum (or appeal to popularity) fallacy and appeal to authority fallacy are similar in that they both conflate the validity of a belief with its popular acceptance among a specific group. However there is a key difference between the two:

• An ad populum fallacy tries to persuade others by claiming that something is true or right because a lot of people think so.
• An appeal to authority fallacy tries to persuade by claiming a group of experts believe something is true or right, therefore it must be so.

To identify a false cause fallacy , you need to carefully analyse the argument:

• When someone claims that one event directly causes another, ask if there is sufficient evidence to establish a cause-and-effect relationship. 
• Ask if the claim is based merely on the chronological order or co-occurrence of the two events. 
• Consider alternative possible explanations (are there other factors at play that could influence the outcome?).

By carefully analysing the reasoning, considering alternative explanations, and examining the evidence provided, you can identify a false cause fallacy and discern whether a causal claim is valid or flawed.

False cause fallacy examples include: 

• Believing that wearing your lucky jersey will help your team win 
• Thinking that everytime you wash your car, it rains
• Claiming that playing video games causes violent behavior 

In each of these examples, we falsely assume that one event causes another without any proof.

The planning fallacy and procrastination are not the same thing. Although they both relate to time and task management, they describe different challenges:

• The planning fallacy describes our inability to correctly estimate how long a future task will take, mainly due to optimism bias and a strong focus on the best-case scenario.
• Procrastination refers to postponing a task, usually by focusing on less urgent or more enjoyable activities. This is due to psychological reasons, like fear of failure.

In other words, the planning fallacy refers to inaccurate predictions about the time we need to finish a task, while procrastination is a deliberate delay due to psychological factors.

A real-life example of the planning fallacy is the construction of the Sydney Opera House in Australia. When construction began in the late 1950s, it was initially estimated that it would be completed in four years at a cost of around $7 million.

Because the government wanted the construction to start before political opposition would stop it and while public opinion was still favorable, a number of design issues had not been carefully studied in advance. Due to this, several problems appeared immediately after the project commenced.

The construction process eventually stretched over 14 years, with the Opera House being completed in 1973 at a cost of over $100 million, significantly exceeding the initial estimates.

An example of appeal to pity fallacy is the following appeal by a student to their professor:

“Professor, please consider raising my grade. I had a terrible semester: my car broke down, my laptop got stolen, and my cat got sick.”

While these circumstances may be unfortunate, they are not directly related to the student’s academic performance.

While both the appeal to pity fallacy and   red herring fallacy can serve as a distraction from the original discussion topic, they are distinct fallacies. More specifically:

• Appeal to pity fallacy attempts to evoke feelings of sympathy, pity, or guilt in an audience, so that they accept the speaker’s conclusion as truthful.
• Red herring fallacy attempts to introduce an irrelevant piece of information that diverts the audience’s attention to a different topic.

Both fallacies can be used as a tool of deception. However, they operate differently and serve distinct purposes in arguments.

Argumentum ad misericordiam (Latin for “argument from pity or misery”) is another name for appeal to pity fallacy . It occurs when someone evokes sympathy or guilt in an attempt to gain support for their claim, without providing any logical reasons to support the claim itself. Appeal to pity is a deceptive tactic of argumentation, playing on people’s emotions to sway their opinion.

Yes, it’s quite common to start a sentence with a preposition, and there’s no reason not to do so.

For example, the sentence “ To many, she was a hero” is perfectly grammatical. It could also be rephrased as “She was a hero to  many”, but there’s no particular reason to do so. Both versions are fine.

Some people argue that you shouldn’t end a sentence with a preposition , but that “rule” can also be ignored, since it’s not supported by serious language authorities.

Yes, it’s fine to end a sentence with a preposition . The “rule” against doing so is overwhelmingly rejected by modern style guides and language authorities and is based on the rules of Latin grammar, not English.

Trying to avoid ending a sentence with a preposition often results in very unnatural phrasings. For example, turning “He knows what he’s talking about ” into “He knows about what he’s talking” or “He knows that about which he’s talking” is definitely not an improvement.

No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing . While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency , relevance , authority , accuracy , and purpose . ChatGPT fails to meet at least three of them:

• Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.
• Authority: It’s just a language model and is not considered a trustworthy source of factual information.
• Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources .

So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons – for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source .

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.

So ChatGPT is not always trustworthy . It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.

Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source .

No, it is not possible to cite your sources with ChatGPT . You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.

Instead, use a tool designed for this purpose, like the Scribbr Citation Generator .

But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.

GPT  stands for “generative pre-trained transformer”, which is a type of large language model: a neural network trained on a very large amount of text to produce convincing, human-like language outputs. The Chat part of the name just means “chat”: ChatGPT is a chatbot that you interact with by typing in text.

The technology behind ChatGPT is GPT-3.5 (in the free version) or GPT-4 (in the premium version). These are the names for the specific versions of the GPT model. GPT-4 is currently the most advanced model that OpenAI has created. It’s also the model used in Bing’s chatbot feature.

ChatGPT was created by OpenAI, an AI research company. It started as a nonprofit company in 2015 but became for-profit in 2019. Its CEO is Sam Altman, who also co-founded the company. OpenAI released ChatGPT as a free “research preview” in November 2022. Currently, it’s still available for free, although a more advanced premium version is available if you pay for it.

OpenAI is also known for developing DALL-E, an AI image generator that runs on similar technology to ChatGPT.

ChatGPT is owned by OpenAI, the company that developed and released it. OpenAI is a company dedicated to AI research. It started as a nonprofit company in 2015 but transitioned to for-profit in 2019. Its current CEO is Sam Altman, who also co-founded the company.

In terms of who owns the content generated by ChatGPT, OpenAI states that it will not claim copyright on this content , and the terms of use state that “you can use Content for any purpose, including commercial purposes such as sale or publication”. This means that you effectively own any content you generate with ChatGPT and can use it for your own purposes.

Be cautious about how you use ChatGPT content in an academic context. University policies on AI writing are still developing, so even if you “own” the content, you’re often not allowed to submit it as your own work according to your university or to publish it in a journal.

ChatGPT is a chatbot based on a large language model (LLM). These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.

ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes.

Essentially, ChatGPT’s answers are based on predicting the most likely responses to your inputs based on its training data, with a reward system on top of this to incentivise it to give you the most helpful answers possible. It’s a bit like an incredibly advanced version of predictive text. This is also one of ChatGPT’s limitations : because its answers are based on probabilities, they’re not always trustworthy .

OpenAI may store ChatGPT conversations for the purposes of future training. Additionally, these conversations may be monitored by human AI trainers.

Users can choose not to have their chat history saved. Unsaved chats are not used to train future models and are permanently deleted from ChatGPT’s system after 30 days.

The official ChatGPT app is currently only available on iOS devices. If you don’t have an iOS device, only use the official OpenAI website to access the tool. This helps to eliminate the potential risk of downloading fraudulent or malicious software.

ChatGPT conversations are generally used to train future models and to resolve issues/bugs. These chats may be monitored by human AI trainers.

However, users can opt out of having their conversations used for training. In these instances, chats are monitored only for potential abuse.

Yes, using ChatGPT as a conversation partner is a great way to practice a language in an interactive way.

Try using a prompt like this one:

“Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: [conversation topic].”

Yes, there are a variety of ways to use ChatGPT for language learning , including treating it as a conversation partner, asking it for translations, and using it to generate a curriculum or practice exercises.

AI detectors aim to identify the presence of AI-generated text (e.g., from ChatGPT ) in a piece of writing, but they can’t do so with complete accuracy. In our comparison of the best AI detectors , we found that the 10 tools we tested had an average accuracy of 60%. The best free tool had 68% accuracy, the best premium tool 84%.

Because of how AI detectors work , they can never guarantee 100% accuracy, and there is always at least a small risk of false positives (human text being marked as AI-generated). Therefore, these tools should not be relied upon to provide absolute proof that a text is or isn’t AI-generated. Rather, they can provide a good indication in combination with other evidence.

Tools called AI detectors are designed to label text as AI-generated or human. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.

But these tools can’t guarantee 100% accuracy. Check out our comparison of the best AI detectors to learn more.

You can also manually watch for clues that a text is AI-generated – for example, a very different style from the writer’s usual voice or a generic, overly polite tone.

Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.

While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.

Try the QuillBot summarizer for free

Deep learning requires a large dataset (e.g., images or text) to learn from. The more diverse and representative the data, the better the model will learn to recognise objects or make predictions. Only when the training data is sufficiently varied can the model make accurate predictions or recognise objects from new data.

Deep learning models can be biased in their predictions if the training data consist of biased information. For example, if a deep learning model used for screening job applicants has been trained with a dataset consisting primarily of white male applicants, it will consistently favour this specific population over others.

A good ChatGPT prompt (i.e., one that will get you the kinds of responses you want):

• Gives the tool a role to explain what type of answer you expect from it
• Is precisely formulated and gives enough context
• Is free from bias
• Has been tested and improved by experimenting with the tool

ChatGPT prompts are the textual inputs (e.g., questions, instructions) that you enter into ChatGPT to get responses.

ChatGPT predicts an appropriate response to the prompt you entered. In general, a more specific and carefully worded prompt will get you better responses.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up”. You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs £16 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

You can access ChatGPT by signing up for a free account:

• Follow this link to the ChatGPT website.
• Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
• Type a prompt into the chat box to get started!

A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.

According to OpenAI’s terms of use, users have the right to reproduce text generated by ChatGPT during conversations.

However, publishing ChatGPT outputs may have legal implications , such as copyright infringement.

Users should be aware of such issues and use ChatGPT outputs as a source of inspiration instead.

According to OpenAI’s terms of use, users have the right to use outputs from their own ChatGPT conversations for any purpose (including commercial publication).

However, users should be aware of the potential legal implications of publishing ChatGPT outputs. ChatGPT responses are not always unique: different users may receive the same response.

Furthermore, ChatGPT outputs may contain copyrighted material. Users may be liable if they reproduce such material.

ChatGPT can sometimes reproduce biases from its training data , since it draws on the text it has “seen” to create plausible responses to your prompts.

For example, users have shown that it sometimes makes sexist assumptions such as that a doctor mentioned in a prompt must be a man rather than a woman. Some have also pointed out political bias in terms of which political figures the tool is willing to write positively or negatively about and which requests it refuses.

The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts . It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.

Information extraction  refers to the process of starting from unstructured sources (e.g., text documents written in ordinary English) and automatically extracting structured information (i.e., data in a clearly defined format that’s easily understood by computers). It’s an important concept in natural language processing (NLP) .

For example, you might think of using news articles full of celebrity gossip to automatically create a database of the relationships between the celebrities mentioned (e.g., married, dating, divorced, feuding). You would end up with data in a structured format, something like MarriageBetween(celebrity 1 ,celebrity 2 ,date) .

The challenge involves developing systems that can “understand” the text well enough to extract this kind of data from it.

Knowledge representation and reasoning (KRR) is the study of how to represent information about the world in a form that can be used by a computer system to solve and reason about complex problems. It is an important field of artificial intelligence (AI) research.

An example of a KRR application is a semantic network, a way of grouping words or concepts by how closely related they are and formally defining the relationships between them so that a machine can “understand” language in something like the way people do.

A related concept is information extraction , concerned with how to get structured information from unstructured sources.

Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.

You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.

Yes, you can use ChatGPT to paraphrase text to help you express your ideas more clearly, explore different ways of phrasing your arguments, and avoid repetition.

However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.

Yes, you use ChatGPT to help write your college essay by having it generate feedback on certain aspects of your work (consistency of tone, clarity of structure, etc.).

However, ChatGPT is not able to adequately judge qualities like vulnerability and authenticity. For this reason, it’s important to also ask for feedback from people who have experience with college essays and who know you well. Alternatively, you can get advice using Scribbr’s essay editing service .

No, having ChatGPT write your college essay can negatively impact your application in numerous ways. ChatGPT outputs are unoriginal and lack personal insight.

Furthermore, Passing off AI-generated text as your own work is considered academically dishonest . AI detectors may be used to detect this offense, and it’s highly unlikely that any university will accept you if you are caught submitting an AI-generated admission essay.

However, you can use ChatGPT to help write your college essay during the preparation and revision stages (e.g., for brainstorming ideas and generating feedback).

ChatGPT and other AI writing tools can have unethical uses. These include:

• Reproducing biases and false information
• Using ChatGPT to cheat in academic contexts
• Violating the privacy of others by inputting personal information

However, when used correctly, AI writing tools can be helpful resources for improving your academic writing and research skills. Some ways to use ChatGPT ethically include:

• Following your institution’s guidelines
• Critically evaluating outputs
• Being transparent about how you used the tool

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Please note that the shorter your deadline is, the lower the chance that your previous editor is not available.

If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the  Scribbr Improvement Model  and will deliver high-quality work.

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This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content.

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x == (s || z). You say it kwontized

Rule of Three: advice on writing a PhD thesis

PhD students sometimes get the same bad advice on writing their thesis. I call this advice the Rule of Three . Typically, they get told that their thesis:

• Will take 3 months to write
• Should have 3 results chapters
• Should be 300 pages

These bits of advice have one thing in common: they are all wrong.

• If you have been organised (see below), it should not take 3 months to write a PhD thesis. It certainly shouldn’t involve leaving the lab 3 months before your hand-in date to write up.
• Theses can have one results chapter or they can have more. How many chapters depends on your project, and your results. Trying to make three results chapters out of one chapter ends up in a weak or overlong thesis.
• A thesis is like a piece of string and it will be as long as it needs to be. Aim for brevity and not producing a magnus opus (see below).

Disclaimer: what follows is some different advice. As with all “advice”, your mileage will vary. It is written for the people in my lab but likely applies to UK PhD students doing biomedical research.

Rule 1: aim for a thesis that is good enough

Who will read your thesis? Two people. Your examiners. OK, some parts – such as the Methods section – will be useful to future lab members (although with electronic lab notebooks this function is becoming redundant). Maybe your thesis will be downloaded by someone from the repository, but essentially, it will only be read by your examiners.

How long does an examiner spend reading your thesis? A few hours. One day maximum. They simply have no more time. Do you really want to spend three months of your life writing something that will be read for just a few hours by two people?

It’s for these reasons that spending too much effort writing a perfect thesis is a waste of time. It just needs to be good enough.

As well as being just good enough, it only needs to be long enough . A big mistake students make is to produce a really long thesis because they think that that is what theses should be (rule of 3). What happens is the examiner will receive the thesis, look at how many pages there are, subtract the bibliography, and their heart will sink if it is too long.

You might now be wondering: is writing a thesis a waste of time?

No, because you have to do it to get your PhD.

No, because you learn important writing skills. You also learn how to assemble a large document (it’s often how students learn to use Word properly or up their LaTeX game). It’s good training for writing papers and other technical documents down the line. Employers know this when they hire you.

But that is about it. So you just need to write something that is good enough to pass.

Rule 2: prioritise papers and the thesis will follow

Papers are the priority. They are more useful to you and to your PI. But this advice isn’t motivated by self-interest. If you go into the viva and the work in your thesis is already peer reviewed and published, it’s harder for the examiners to criticise it. At least, they will not approach your thesis with the question: is this work publishable? This is one criteria for passing your PhD, so demonstrating that it is publishable means you are (almost) there.

This was the one bit of advice I received when doing my PhD and it is still true today. OK, it is harder these days to get a first author paper published before you submit your thesis. However a preprint on bioRxiv before you begin writing will help you to prepare your thesis and will still tick the publishable box.

How long should it take to write your thesis?

There is tension here because you are at your most useful in the lab as you near the end of your PhD. One week of labwork now is worth one month (or more) earlier in your PhD. You are most valuable to your lab/PI/science/career at this point and keeping working in the lab will yield more rewards. But it won’t get your thesis written.

The first bit of writing is busywork and can be done around lab work. “Deep writing” and reading does need time away for most students.

If you have only collected data in the lab and not analysed it, if you’ve not presented your work very often, if you are disorganised… yes, it will take you a full three months to write your thesis.

All the folks in my lab are encouraged to get figures ready, analyse as they go and they also give regular talks. It should not take anyone in this position three months away from the lab to write their thesis.

Agreeing a timeline with your PI for when you begin writing is really important. Regular deadlines and a commitment to timely feedback from your supervisor make thesis writing easier. The discussion needs to be based on facts though. Often students want to budget a lot of time to writing, because of the rule of 3 or because they believe they are “bad at writing”. It helps to see some evidence. Writing draft chapters earlier in the PhD – which is a requirement at some universities – can reveal difficulties and weaknesses.

Reality check

If you hear the rule of 3 from everyone and your supervisor is giving you different advice. It might be time for a reality check. Have a look at past theses from the lab. How long were they? How many chapters? Information is good.

You can see that all theses are fewer than 300 pages in length, many substantially so. Four have three chapters and two have two. Although looking closer, two of the theses with three chapters use a results chapter as an expanded methods chapter.

Ultimately, the thesis is your work but you will get input from your supervisor. Regardless of what is written here or how many people tell you about the rule of 3, your supervisor will have their own ideas about how your thesis should be. Agreeing a sensible plan with them is the way to get started productively.

Getting started

This is not a comprehensive guide but in order to write a good enough thesis, you first need a plan.

• Make a figure list. This should be every single figure you can think of. You can cross off ones you don’t need later if they don’t fit or are insignificant.
• Plan the narrative. There is usually more than one way to put together the figures to make a thesis. Be prepared for this to change after you start writing! Sometimes the writing process reveals ways in which the narrative should be rearranged.
• How many results chapters? Start with the idea that you will have one. Does it need dividing? If yes, then what are the titles of the two chapters? If you have difficulty titling them you may need to split to a 3rd.

Now you have a plan. It’s time to get going.

Set some goals – but make them small. Having a goal of “I am going to complete my thesis” is too demoralising. You need to feel like you are making progress constantly to stay motivated. Break it down into smaller chunks. “I will finish this chapter by next Friday”. “I will write the cloning section this morning and then go for a walk”.

Write the materials and methods first . It’s the easiest bit to write because it is all technical writing with little wordcraft required. You can fit it around labwork. In fact, it is easier to write whilst in the lab because you can look up all the stuff you need. Importantly, it gets over the “blank page syndrome”.

Next get your figures together . This should already be done if you have been organised.

Then write the figure legends . You already have the title for each figure from your plan. All you need to do is describe each panel. Again, quite low energy writing required for this task.

Now write the results sections ! This is the same way that we put papers together. The results parts of the thesis are more extended but in principle you will guide the reader though the figures that you’ve made. Remember, you already have the legends written. So you are already partly on your way.

Time to regroup . At this point you can refine your plan for the introduction and check the rest of your plan still makes sense. Now is the time for some deep writing and reading.

The post title comes from “Rule of Three” by The Lemonheads.

Frequently asked questions

How long does it take to get a phd.

This varies by country. In the United States, PhDs usually take between 5–7 years: 2 years of coursework followed by 3–5 years of independent research work to produce a dissertation.

In the rest of the world, students normally have a master’s degree before beginning the PhD, so they proceed directly to the research stage and complete a PhD in 3–5 years.

Frequently asked questions: Graduate school

In the US, most graduate school applications require you to include:

• Transcripts from previous educational institutions
• Standardized test scores (such as the GRE or MCAT)
• A graduate resume
• 2–3 letters of recommendation
• A statement of purpose

Some programs may ask you to write a personal statement in addition to, or instead of, a statement of purpose. You may also be asked to an interview .

Always carefully read the application instructions for the specific program you’re applying to.

Most medical school programs interview candidates, as do many (though not all) leading law and business schools.

In research programs, it depends—PhDs in business usually do, while those in economics normally do not, for example.

Some schools interview everyone, while others only interview their top candidates. Look at the websites of the schools you’re applying to for more information on whether they conduct interviews.

In addition to thinking about your answers for the most commonly asked grad school interview questions , you should reach out to former and current students to ask their advice on preparing and what sort of questions will be asked.

Look back through your resume and come up with anecdotes that you could use for common questions, particularly those that ask about obstacles that you overcame. If you’re applying for a research program, ensure that you can talk about the previous research experience you’ve had.

You should also read as much research in your field as possible. Research the faculty at the schools you’re applying to and read some of their papers. Come up with a few questions that you could ask them.

Graduate schools often ask questions about why you are interested in this particular program and what you will contribute.

Try to stay away from cliche answers like “this is a good program” or “I got good grades in undergrad” and focus instead on the unique strengths of the program or what you will bring to the table. Understand what the program is looking for and come up with anecdotes that demonstrate why you are a good fit for them.

Different types of programs may also focus on different questions:

• Research programs will often ask what topics you’d like to research and who you would like to work with, as well as specific questions about your research background.
• Medical schools are interested in your personal motivation, qualities such as integrity and empathy, and how you’d respond to common ethical dilemmas.
• Business schools will focus on your past work experience and future career prospects, and may be particularly interested in any experience you have managing or working with others.

Some students apply to graduate school straight from undergrad, but it’s also common to go back to school later in life. The ideal time to do so depends on various financial, personal, and career considerations . Graduate school is a big commitment, so you should apply at a time when you can devote your full attention to it.

Your career path may also determine when you should apply. In some career fields, you can easily progress without a graduate degree, while in others—such as medicine, business, and law—it’s virtually impossible to move up the career ladder without a specific graduate degree.

Most graduate school applications for American graduate programs are due in December or January for a September start.

Some types of programs, especially law school, are rolling applications, meaning that the earlier you apply, the earlier you’ll hear back. In this case, you should aim to apply as early as possible to maximize your chances.

Medical school follows a completely separate timeline with much earlier deadlines. If you’re applying for medical school, you should speak to advisors at your university for more information.

A good starting point to aim for is about 18 months before you would start the program, or 6–9 months before the applications are due.

In the first few months of the process, research programs and study for any standardized exams you might need.

You can then begin writing your personal statements and statements of purpose , as well as contacting people to write your letters of recommendation . Ensure that you give recommenders plenty of time to complete their letters (ideally around 2–4 months).

In the US, the graduate school application process is similar whether you’re applying for a master’s or a PhD . Both require letters of recommendation , a statement of purpose or personal statement , a resume or CV , and transcripts. Programs in the US and Canada usually also require a certain type of standardized test—often the GRE.

Outside the US, PhD programs usually also require applicants to write a research proposal , because students are expected to begin dissertation research in the first year of their PhD.

A master’s degree usually has a higher upfront cost, but it also allows you to start earning a higher salary more quickly. The exact cost depends on the country and the school: private universities usually cost more than public ones, and European degrees usually cost less than North American ones. There are limited possibilities for financial aid.

PhDs often waive tuition fees and offer a living stipend in exchange for a teaching or research assistantship. However, they take many years to complete, during which time you earn very little.

This depends on the country. In the United States, you can generally go directly to a PhD  with only a bachelor’s degree, as a master’s program is included as part of the doctoral program.

Elsewhere, you generally need to graduate from a research-intensive master’s degree before continuing to the PhD.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

It’s best to ask in person if possible, so first reach out and request a meeting to discuss your graduate school plans.

Let the potential recommender know which programs you’re applying to, and ask if they feel they can provide a strong letter of recommendation . A lukewarm recommendation can be the kiss of death for an application, so make sure your letter writers are enthusiastic about recommending you and your work!

Always remember to remain polite. Your recommenders are doing you a favor by taking the time to write a letter in support of your graduate school goals.

This depends on the program that you are applying for. Generally, for professional programs like business and policy school, you should ask managers who can speak to your future leadership potential and ability to succeed in your chosen career path.

However, in other graduate programs, you should mostly ask your former professors or research supervisors to write your recommendation letters , unless you have worked in a job that corresponds closely with your chosen field (e.g., as a full-time research assistant).

Choose people who know your work well and can speak to your ability to succeed in the program that you are applying to.

Remember, it is far more important to choose someone who knows you well than someone well-known. You may have taken classes with more prominent professors, but if they haven’t worked closely with you, they probably can’t write you a strong letter.

The sections in your graduate school resume depend on two things: your experience, and the focus of the program you’re applying to.

Always start with your education. If you have more than one degree, list the most recent one first.

The title and order of the other sections depend on what you want to emphasize. You might include things like:

• Professional experience
• Voluntary and extracurricular activities
• Publications
• Awards and honors
• Skills and certifications

The resume should aim for a balance between two things: giving a snapshot of what you’ve done with your life so far, and showing that you’re a good candidate for graduate study.

A resume is typically shorter than a CV, giving only the most relevant professional and educational highlights.

An academic CV should give full details of your education and career, including lists of publications and presentations, certifications, memberships, grants, and research projects. Because it is more comprehensive, it’s acceptable for an academic CV to be many pages long.

Note that, outside of the US, resume and CV are often used interchangeably.

No, don’t include your high school courses and grades. The education section should only detail your college education.

If you want to discuss aspects of high school in your graduate school application, you can include this in your personal statement .

A resume for a graduate school application is typically no more than 1–2 pages long.

Note, however, that if you are asked to submit a CV (curriculum vitae), you should give comprehensive details of all your academic experience. An academic CV can be much longer than a normal resume.

Always carefully check the instructions and adhere to any length requirements for each application.

If you’re applying to multiple graduate school programs, you should tailor your personal statement to each application.

Some applications provide a prompt or question. In this case, you might have to write a new personal statement from scratch: the most important task is to respond to what you have been asked.

If there’s no prompt or guidelines, you can re-use the same idea for your personal statement – but change the details wherever relevant, making sure to emphasize why you’re applying to this specific program.

If the application also includes other essays, such as a statement of purpose , you might have to revise your personal statement to avoid repeating the same information.

The typical length of a personal statement for graduate school applications is between 500 and 1,000 words.

Different programs have different requirements, so always check if there’s a minimum or maximum length and stick to the guidelines. If there is no recommended word count, aim for no more than 1-2 pages.

A statement of purpose is usually more formal, focusing on your academic or professional goals. It shouldn’t include anything that isn’t directly relevant to the application.

A personal statement can often be more creative. It might tell a story that isn’t directly related to the application, but that shows something about your personality, values, and motivations.

However, both types of document have the same overall goal: to demonstrate your potential as a graduate student and s how why you’re a great match for the program.

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The Scribbr Citation Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Citation Generator in our publicly accessible repository on Github .

Emeritus Professor, Edinburgh Napier University

How long does it take to write a PhD thesis?

My short answer is 68 days, but please read the detail below…

Bold resolutions: “Piled Higher and Deeper” by Jorge Cham www.phdcomics.com

As a PhD supervisor I have often been asked ‘How long do you think it will take me to write up my thesis?’ My answer always begins ‘It depends…’ We then continue the conversation with an audit of material already drafted that may contribute (in edited format) to the final thesis. These include the initial literature review from the first year transfer report, and posters, conference papers and journal articles presented and/or published from the on-going work.

For example third year PhD student John Mowbray , who is currently based within the Centre for Social Informatics (CSI) at Edinburgh Napier University , has a strong basis for his literature review chapter in the form of a conference paper delivered at CoLIS 2016 , which is due to be published in full in Information Research later this year. Similarly John’s fellow student Frances Ryan has already published an account of research design for her study. This paper will underpin the writing of her methods chapter.

Then we consider less formal sources, such as any discussions or debates that the student has documented publicly elsewhere, for example in blog posts. See, for instance Lyndsey Jenkins ‘ recent thoughts about the importance of research domain at http://lyndseyjenkins.org. These may well contribute to a section of Lyndsey’s methods chapter when she comes to write up her work in 2018.

The students also have ‘non-public’ material about their work that will be adapted for their theses. These include interim reports for their supervisors and/or other stakeholders. For example, last semester CSI PhD student Iris Buunk wrote a report on some of the empirical work that she has conducted for the body that gave her access to survey respondents. Handwritten ideas and remarks kept in notebooks over the course of PhD registration are also very valuable ‘private’ resources.

Once we have completed this audit, the challenge of transforming all the work completed to date into an 80,000 word thesis appears not to be so great – but of course, it still all needs to be done!

Records from writing up my own PhD have also recently served as another source for answering questions about preparing the main output of the doctoral study. I undertook my PhD part-time over a period of just over four years while working full-time. Throughout this period there were weeks when I could not progress my work at all. This was largely due to other commitments in intensive periods related to teaching such as the marking season towards the end of each semester. There were other times when it was much easier to devote myself to my PhD. For example, I took annual leave in University vacation time for this purpose (rather than went away on holiday). To guard against losing track of my PhD at times when I was too busy to devote any time to it I kept detailed notes of my progress. As a result of this, I know exactly how much time I spent writing up each chapter for the final version of the thesis. Although all PhD theses are different, the proportion of time on each type of chapter may be helpful to those who have resolved to submit their theses in 2017.

In total it took me 68 days to write up my thesis (NB 68 to write up the work, not 68 days to complete the PhD!) This is the equivalent of approximately 14 working weeks, assuming a five day week. It needs to be borne in mind, however, that I was a part-time student. In practice the writing up was done over the last seven months of the four and half years in which I worked on the entire doctoral study.

The largest portion of the writing-up time – around three quarters – was spent on the two chapters that related the findings of my research, and about a fifth on the discussion chapter. My literature review took very little time to write up (just 5 days) because I had already presented much of it in published form. The methods and conclusions chapters did not take very long either (3.5 and 2.5 days respectively) largely because their content was straightforward. My introductory chapter was very short at a page and a half and was thus drafted in just a couple of hours.

As might be deduced from the time allocations given above, I found the results and discussion chapters most heavy-going. The former was due to the quantity of empirical data to convert into a fluent account of the findings, and the latter because of the intellectual challenge of expressing the meaning of the findings and how the outcomes of my study represented an original contribution to the domain. However, once these two elements were ‘cracked’ it was a relatively easy task to pull all the other chapters together.

If you are reading this blog post as a PhD student in the later stages of your work, I would advise you to be prepared for the long haul of writing up your results and the discussion chapters, and ensure that you allocate a high proportion of your write-up time to these accordingly. It is also worth noting that I found that the closer I came to the target of completing my write-up, the more important it was for me to avoid other distractions. You cannot control for all of them (for example, illness), but I would caution against getting actively involved in anything that will take you away from your PhD at this intensive stage, such as planning a big event (for example, a major holiday, a house move, or a family wedding) or starting a new job.

If you are still in the early stages of your doctoral study, my first piece of advice is to plan your conference participation and journal paper publishing activity with the final thesis in mind. Be selective and strategic so that you prioritise engagement in external events that are valuable to the completion of your thesis and/or your future career. Each piece of work that you present externally should progress your study by encouraging you to write-up as you go along (for example in the form of a poster, a set of slides, a full paper), defend your ideas in person within your academic community, seek feedback on work completed to date, and solicit advice on the later stages. You should also be documenting any thoughts or ideas that may be valuable to writing up in a format that make sense to you, whether this be in a set of handwritten notes or in a more public format such as a series of structured blog posts.

Good luck to all those who will submit their theses in 2017!

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The Savvy Scientist

Experiences of a London PhD student and beyond

How Long Does It Take To Get A PhD?

If you’ve ever found yourself wondering, “How long does it take to get a PhD” you’re not alone. Before I started mine I thought I’d be working right up until the university’s four year deadline to submission. However it is often possible to submit a PhD faster, though you do have to consider the time for your viva and corrections.

In this post we’ll go through each of the different stages of a PhD. We’ll also look at how much time a PhD takes up in the wider context of your career as a whole.

At the end you can find a timeline showing how long I personally took to go through each stage on the journey to getting my own PhD!

How long does it take to get a PhD when studying full time?

In short, full-time PhD students in the UK will typically take 3-4 years to submit their PhD thesis. Then it’s a few more months before the viva (oral exam) takes place. PhDs in many other European countries and certain commonwealth countries such as Australia, take a similar length of time. For other countries such as the US it will typically be a year or two longer than this, which could be a positive or negative depending on why you’re doing a PhD !

If you’re considering applying for a PhD, here are some relevant posts for you which may be useful:

• How to apply for a PhD
• PhD FAQs – A Complete Beginner’s Guide to Doctoral Study
• How much work is a PhD?

Putting a PhD in career context

Still on the fence as to whether you really want to spend roughly four years of your life on a PhD?

Let’s put it in perspective.

If you come out of uni at age 22 and retire at age 68, you’ll have a career of approximately 46 years:

Let’s say after a few years of working you decide to do a PhD. We’ll assume in total it takes 4.5 years – about the maximum length of time a full-time PhD can be from start to end in the UK.

Now let’s slot in that PhD in orange:

This shows the average PhD-holder still has about a 40 year career ahead of them if they were to work until state pension age in the UK! So doing a PhD lowers your career by 4-4.5 years, or as a percentage: less than 10%!

In reality, retirement age is very likely to be pushed well into the 70s by that time, making the time spent on a PhD even less of an issue. Sidenote: while you’re young take the opportunity to build up financial freedom !

Is a PhD worth it? I’ve delved into this more here , but it will depend a bit on your own circumstances so I can’t definitively answer that for you. But it’s important to recognise that doing a PhD isn’t taking much time away from your career and could potentially add a lot to it!

Read on for details on why a PhD takes around four years to complete.

The structure of a PhD

There are four parts to getting a PhD:

• The main one: working on your project and doing the research!
• Writing up your work and submitting it as a thesis (or papers if your university allows it)
• The oral examination (viva)
• Making any corrections to the thesis requested by the examiners and submitting the final version to the university

Let’s quickly cover each one of these parts in a little more detail:

1. PhD project: Carrying out research

This section is the core of your PhD and the bit where you actually do some research. Some people may divide the PhD project into sections such as:

• Reading literature
• Deciding on a research project direction
• Trying out different experimental methods
• Main experiments & data collection

Every project (and person) is so different that it would be useless to give you a generic timeline with how long each section could take. Points 2 and 3 may even appear in different orders depending on your project. There is no convention! In addition, rarely will someone carry these tasks out in series with no overlap between them. Working on tasks in parallel helps to keep your days more varied and interesting too.

If you’ve already got a very well defined project you may whiz through the first three points. The same goes for if you’re taking over from a previous student who has already got your project going. For my project I was pretty much starting from scratch in a lot of regards. I also had a scholarship with a lot of freedom, so spent more time on these preliminary stages. It really varies a lot, so let’s just cover how long the research typically lasts in total:

PhD research duration

Two main factors will typically dictate how long is spent on research before writing up:

• Funding : The majority of PhD students in STEM are funded (post on how here , and how much here ) and no one wants to be supporting themselves once the money runs out. Therefore usually PhD students will aim to complete all research (and ideally writing up) by the time their funding ceases. Most PhD projects in the UK are funded for 3 – 3.5 years . The maximum is four years, for reasons you’ll see in a moment. Personally my scholarship was for 3 years and then I was separately employed for another five months by my supervisor.
• Submission deadline : You can imagine that the completion rate of PhDs isn’t great once funding ceases. You’re seeking employment to support yourself and suddenly the PhD isn’t a focus. More and more universities are now imposing strict deadlines for submission to boost the chances of students successfully completing the degree. This deadline to submit your thesis is often four years from your start date .

A student will typically work back from these deadlines, estimating how long is required for writing up and spend all of the remaining time doing research.

Approximate research duration: Overall, from induction through to finishing data analysis will typically take 2.5 – 3.5 years.

2. Writing up

As we covered above, there are two main factors which will usually control how much time is available for the project as a whole. It is up to the student to decide how late they leave it to finish conducting research and start writing up.

For some people writing up can take a significant number of months. This may be particularly true for non-native speakers.

My somewhat controversial suggestion is to leave it as late as possible to go into the full-time writing-up stage. This allows you to maximise the amount of time you’re able to access labs or any specialist resources which will disappear once you leave. It’s also very easy to procrastinate whilst writing, therefore by constraining the timeline you’re less likely to waste your time.

However not officially being in the ‘writing-up’ stage, doesn’t mean doing no writing at all in advance. In fact let’s answer a few common questions related to writing up:

Can you write up as you go along?

Yes! You can of course write whenever you fancy it, but additionally typically there will be a few assessment checkpoints during the research phase of your project. For the first of them, which occurs around 9 months after starting the project, you’ll usually have to produce a report. This often includes the rationale for the project, a literature review and your progress so far. At Imperial it was called the Early Stage Assessment (ESA) and others know it as the upgrade interview.

Even if you do no other writing up during your project, this report can still form a useful starting point for your thesis. My report was just under 20 pages long and the literature review component formed the foundations for the literature review in my PhD thesis.

There is often a second checkmark at 18-24 months (called the “Late stage assessment” or “LSR” at Imperial). This may again may include a formal report which could go some way towards your thesis. The alternative to writing a report for the LSR is showing a paper you’ve submitted to a journal: a handy reason to write papers, which we’ll come onto more in a second.

You can take it further and write up chapters of your work as you go if you want. The most obvious reason for doing so it to produce papers of each results chapter which we’ll now discuss.

What about papers?

I strongly recommend PhD students submit papers during their PhD. I only had one published by the time of my viva and that single paper was immensely valuable in providing evidence that I’d done novel research of a high enough standard. See my guide to writing your first paper here:

• Writing an academic journal paper

If nothing else, writing up your work as papers serves as a useful way to split your PhD work into sections which can then become chapters of a thesis.

Some universities may allow you to submit a string of papers instead of a thesis. I know that this is common in other parts of Europe.

By the time I submitted my thesis I had one results chapter accepted as a paper, another two papers roughly drafted and another chapter I hadn’t really worked on. At Imperial you do have to submit a thesis, but crucially the content can include submissions for papers. I had to do a bit of work converting papers into chapters but it was minimal, maybe a few days work.

Your mileage may vary: I know that some other universities don’t allow you to copy text (or figures) word for word from papers due to plagiarism, which of course suddenly creates a lot of extra work. Either way, writing up your research as papers during a PhD is strongly encouraged if you can!

Should you care about having a ‘nice looking’ thesis?

This is a question only you can answer. If you want a thesis formatted wonderfully in LaTex with stunning figures etc you’ll of course have to dedicate additional time to writing the thesis.

For me I didn’t (and don’t) care at all about having a nice looking thesis. I’d much rather invest energy in writing things people will actually read: papers.

Do remember that what you submit to the examiners is merely a draft and isn’t your last chance to make changes!

Approximate duration for writing up: Typically 2 – 6 months, but it doesn’t have to take this long!

3. Scheduling and passing the oral examination (viva)

When you’re coming to the end of your PhD you’ll discuss potential examiners with your supervisor and begin organising potential dates for your exam: known as a viva here in the UK. In the UK you’ll usually have a minimum of one external examiner (from another university) and one internal examiner (from your university).

The date will usually be a minimum of two months after you submit your thesis. You can potentially get away with a bit shorter, but universities don’t like doing it in case of any holdups. It’ll ultimately come down to finding a suitable time which works for your examiners.

I know of people who’ve had to wait a lot longer than a few months because of difficulties finding a time which works for all the examiners, especially if substantial travel is required. One of the perks of the current C-19 working from home arrangement is that examinations may be more easily scheduled.

The oral exam itself is only one day. I’m putting together a separate post to cover what the viva experience is like.

Approximate duration for scheduling and passing the exam: Typically three months.

4. Making any corrections to the thesis

The examination will in part focus on the quality of your written thesis. Some people pass the viva with no corrections. Therefore you could simply use the copy you sent to the examiners as your final copy of the thesis, requiring no further work.

However it is rare to pass with no corrections and instead it is likely that there will be “minor corrections” suggested to improve the thesis. These corrections may be to:

• Improve clarity
• Add some further necessary detail, including potential additional analysis
• Removing unsubstantiated claims
• Correcting typos and grammatical errors

It is also possible for “major corrections” to be requested, requiring much more substantial work – usually including further experiments.

In the case of minor corrections, sometimes the examiners will want to see the updated thesis, other times they’re happy to let your supervisor approve the changes.

Typically in the UK you are given a deadline to submit these corrections within three months of your viva . Of course it usually doesn’t actually take anywhere near three months of solid work to make the changes, but the examiners and university recognise that by then you’ll have ceased being a PhD student and likely have other commitments. Plus, not everyone wants to dive straight back in after the viva!

Approximate duration for making changes to your thesis: 0 – 3 months.

5. Awaiting confirmation of your PhD

Once you’ve submitted your final copy of the thesis you’ll have to wait for the registry team to formally confirm your PhD. At Imperial this is done once per month. And you’ll get an email something like this:

Duration: one month or less.

So how long does it take to get a PhD in total?

Now we’ve covered all the separate stages of a PhD, hopefully you have a better sense of how long it could take to get a PhD.

Wondering how long is a PhD in the UK? In the UK the average time from starting the PhD to submitting the thesis is approximately three and a half years. It’ll then take another 4-6 months until the viva has been completed, the final thesis copy submitted and for the university to award the PhD. This brings us to a total length of approximately 4 to 4.5 years for a PhD in the UK .

This assumes that study towards the PhD is full-time. Of course if you’re working part-time on the project the duration can increase considerably.

If the PhD is as part of a CDT/DTC programme the timeline may be slightly different, but won’t necessary be much longer. In a CDT you’ll usually spend a year studying for an MSc, followed by three years working towards the PhD, at which point you’ll submit the thesis. Therefore the total time for a PhD as part of a CDT with a MSc+PhD programme may still be just over four years, including the MSc.

My experience

I finished my PhD earlier this year and this is how long each of the PhD stages took me:

* I wanted to arrange a holiday for April ( which didn’t happen ) so knew when I wanted to have the viva and worked back from there. I could have submitted the thesis 7.5 months later and still have been within the four year deadline, but wasn’t interested in stringing it out! We arranged the viva back in January and submitting the thesis six weeks before the viva was about the minimum reasonable period. You may be required to submit the thesis before officially scheduling the viva.

** I’m a fast writer and didn’t massively care about the quality of the thesis but even so, couldn’t write a whole thesis in three weeks! Crucially I had most of the results chapters already written by this point. I spent about a week writing the literature review section, a week on the introduction and discussion chapters and a week putting it together. Once you have your results sections don’t spend ages faffing with the other sections (intro, lit review, discussion), they’ll swallow up as much time as you give them.

*** Had I been able to go away on my two month holiday I’d have left the thesis corrections for when I got back. But since I wasn’t able to go, I decided to crack on with them right away. Most people would tend to have more of a break before submitting the corrected version, but I knew that if I submitted quickly then I’d be in time for the PhD to be confirmed in April – just a few days later. There is also something funny about having a PhD confirmed on April Fools’ Day!

How long does it take to get a PhD outside of the UK?

PhD programmes vary in structure and length around the world. PhDs in mainland Europe and Australia are typically of similar length to the UK. Though if you’re looking to study for a PhD in the US you can often expect to be studying for an extra year or two.

In the US PhDs typically begin with formal courses and lab rotations , with PhD projects not being decided until midway through your second year. This is a similar parallel to undergraduate degrees being less specialised than in Europe. Therefore in the US PhDs typically take 4-6 years to complete.

More details for PhDs around the globe can be found on this handy set of pages FindAPhD has put together.

Hopefully you’re now able to answer how long it takes to get a PhD. If you have any other PhD-related questions you’d like answered please let me know and I’ll be happy to address them !

Footnotes for the lifetime graphic: * UK life expectancy for a current 20 year old is estimated to be around 87 years. * The figure was inspired by some of the amazing posts on Wait but Why . If you’ve not already checked out the website, please do!

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The PhD Discussion Chapter: What It Is & How To Write It

Sep 11, 2023

Your PhD discussion chapter is your thesis’s intellectual epicenter. Think of it as the scholarly equivalent of a courtroom closing argument, where you summarise the evidence and make your case. Perhaps that’s why it’s so tricky – the skills you need in your discussion chapter aren’t skills you’ve likely had to deploy before: it’s where you start to speak like a Doctor.

In this guide, I want to present a comprehensive guide to the PhD discussion chapter. We’ll look at a number of key topics:

What is the purpose of a PhD Discussion Chapter?

• Suggested outlines for a discussion chapter:

Advice for improving your discussion chapter

This is not a normal blog subscription.

Each week we send two short, thought-provoking emails that will make you think differently about what it means to be a PhD student. It is designed to be read in thirty seconds and thought about all day.

  The PhD discussion chapter is the place where your findings, research questions, literature, theoretical framework and methodology coalesce into a coherent narrative. A common pitfall is when students see the discussion chapter as a summary of everything that has come before. This isn’t the case. Instead, the PhD discussion chapter offers a deep, analytical synthesis of your research, providing context, interpretation, and evaluation of your findings.

It’s the place in which you engage with existing theories, explore the significance of your work, and directly address the “So What?” question, highlighting the real-world implications and academic contributions of your research.

 Let’s dig down into each of these things.

Summarising and explaining the research

Before you launch into the detail, start by laying out your findings in a clear, easy to follow way. This is typically done in the introduction and the first proper section of the chapter.

Starting the PhD discussion chapter by clearly laying out your findings serves as an anchor for your reader and sets the stage for the more complex discussions that follow. This foundational step ensures that the reader is equipped with all the necessary information to fully grasp the significance and implications of your work. It’s akin to laying the groundwork before building a complex structure; without a solid base, the intricate analyses may lose their impact or be misunderstood.

For example, if you’re a PhD student in environmental science studying the effects of a specific pollutant on marine life, begin by presenting the key data points, such as the pollutant concentration levels in various regions and the corresponding health indices of marine species studied. Use tables, figures, or graphs to help visualise the data and make it more accessible.

• Laying out Quantitative Findings : If your research is quantitative, use statistical measures to present your results. Clearly state the metrics you’ve considered, such as means, variances, p-values, etc., and what they imply about your research question.
• Laying out Qualitative Findings : In case of qualitative research, such as ethnographic studies or interviews, narrate the trends, patterns, or themes that have emerged. Use representative quotes or observations as illustrative examples.
• Mixed-Methods Approach : If you’ve used both quantitative and qualitative methods, start by outlining how these different types of data will be integrated in your discussion. This could involve presenting the qualitative findings as a contextual backdrop for quantitative data or vice versa.

Remember, your objective at this initial stage is not to overwhelm the reader with complexity but to build a transparent, easily-followable narrative of what you’ve found. By starting with a clear presentation of your findings, you’re laying the groundwork for a powerful, credible discussion chapter that can tackle sophisticated analyses and weighty implications, underpinned by a comprehensible and compelling dataset.

There will be a necessary degree of overlap and repetition between this section (and the discussion chapter in general) and the findings chapter. However, there’s a subtle difference in the way in which the data is introduced in the findings and discussion chapters .

In the findings chapter, you’re generally presenting raw data or observations without interpreting what they mean. In the Discussion chapter, you take those same findings and begin to explore their implications, relate them to existing theories, and evaluate their significance. The danger, however, lies in creating excessive repetition between the two chapters, which can fatigue the reader and dilute the impact of your arguments.

To mitigate this, consider employing the following strategies:

• Selective Highlighting : Choose only the most critical findings to revisit in the Discussion chapter. You don’t need to regurgitate every data point, only those central to the questions you aim to answer in this chapter.
• Narrative Framing : When you bring up a finding in the Discussion chapter, introduce it as a stepping stone to a broader point or argument, rather than an isolated fact. This technique helps the reader understand why you’re revisiting this information and what new aspects you’ll be unveiling.
• Use Different Presentation Formats : If the Findings chapter is heavy on tables and figures, consider summarising key points in a narrative form in the Discussion chapter or vice versa.

By thoughtfully selecting what to revisit and framing it within a new context, you can transform what might appear as repetition into a coherent and evolving narrative that adds value to your thesis. Read more about the difference between the findings and discussion chapters here .

Interpreting and Contextualising Results 

It’s in the discussion chapter that you offer the interpretation and context for your research findings.

Here, you transition from being a data ‘gatherer’ to a data ‘interpreter’, weaving together the threads of research questions, data, methods, literature and theory to tell a complex story. While the Results chapter may offer the “what,” the PhD discussion chapter sheds light on the “why” and “how.” 

For example, if you’re a social scientist studying the effects of social media on mental health, your results chapter might show statistical data indicating a correlation between social media use and anxiety. However, it’s in your discussion chapter that you would compare these findings to existing literature, perhaps linking them to existing theories or debates. This adds a layer of depth and context that transcends the numerical data, inviting academic dialogue and potential future research avenues.

There are three ways in which you can synthesise your findings:

• Interpretation : Begin by interpreting your findings. Use comparisons, contrasts, and correlations to explain the significance of the results. This is where you should also address any unexpected outcomes and explain them.
• Contextualisation : After interpretation, provide a context to situate your findings within the existing body of knowledge. Link back to your Literature Review and Theoretical Framework to show how your research aligns with or diverges from previous work. More on this below.
• Evaluation : Finally, critically evaluate your own research. Discuss its limitations, the implications of your findings, and offer recommendations for future research.

Whether you’re in natural sciences exploring a new chemical compound or in humanities dissecting a piece of classical literature, the discussion chapter is your opportunity to show that your research not only answers specific questions but also contributes to a wider understanding of your field. It’s not enough to say, for instance, that a new drug successfully reduced symptoms of depression in 60% of study participants. You must explore what that 60% means.

• Is it a statistically significant improvement over existing treatments?
• What might be the physiological or psychological mechanisms at work?
• Could your research method have influenced these outcomes?

There’s an art to explaining and synthesising your findings [Link to “How to Explain Your Findings”], but think of it this way: this is where you shine a light on the ‘why’ and ‘how’ of your findings, delving into the nuances that raw data can’t express.

Evaluating Existing Theories and Models  

Beyond explaining your findings, the PhD discussion chapter allows you to evaluate the existing theories and models that you’ve cited in your literature review  and/or theory framework chapter (not sure of the difference? Click here) . Your results could either reinforce established theories or challenge them, both of which significantly contribute to your field.

• For instance, did your research on renewable energy technologies confirm the economic theories suggesting that green energy can be cost-effective?
• Or did your social research provide empirical evidence that contradicts widely held beliefs in your field?

The PhD discussion chapter therefore serves as the space where the theories, concepts, ideas and hypotheses that make up and informed your theory framework and which you touched upon in your literature review intersect with the empirical data you’ve presented.

You’re not just mapping your findings onto the theories and models; you’re dissecting them, affirming or challenging them, and potentially even extending or refining them based on what you’ve discovered.

For instance, if you’re working on a thesis in psychology concerning cognitive development in early childhood, your Literature Review may have discussed Piaget’s stages of cognitive development. However, let’s say your findings indicate some nuances or exceptions to Piaget’s theories, or perhaps children in a certain demographic don’t follow the stages as previously thought.

Your discussion chapter is where you can make the argument that perhaps Piaget’s model, while generally accurate, might require some modification to account for these cases.

• Affirming Theories : If your data aligns closely with the existing theories and models, the PhD discussion chapter serves to strengthen their credibility. Here, you’re lending empirical support to theoretical frameworks.
• Challenging Theories : Alternatively, your findings might contradict or challenge the prevailing theories. This is not a shortcoming; instead, it opens the door for re-evaluation and progress in the field, which is just as valuable.
• Extending or Refining Theories : Perhaps your research uncovers additional variables or conditions that existing models have not accounted for. In such cases, you’re pushing the envelope, extending the current boundaries of understanding.

As you evaluate existing theories and models, be comprehensive yet nuanced. Draw on varied disciplines if relevant. For example, if your thesis is at the intersection of public health and social policy, integrate models from both fields to offer a multi-faceted discussion. Being interdisciplinary can make your discussion richer and more impactful.

Ultimately, the discussion chapter offers you a platform to voice your scholarly interpretation and judgment. You’re participating in a broader academic dialogue, not just narrating your findings but positioning them in a web of knowledge that spans across time, disciplines, and viewpoints.

Discuss Unexpected Results

The discussion chapter is where you also discuss things that didn’t quite work out as planned. In particular, results that were unexpected.

Sometimes the most perplexing data offers the most valuable insights. Don’t shy away from discussing unexpected results; these could be the starting points for future research or even paradigm shifts in your field.

When your research yields findings that diverge from established theories or commonly held beliefs, you’re offered a unique opportunity to challenge and extend existing knowledge.

Take the field of primary education as an illustrative example. Assume you’re researching the efficacy of a specific teaching methodology that prior studies have lauded. However, your data reveals that while the method works wonders for one subgroup of students, it fails to benefit another subgroup. Far from diminishing the value of your research, this unexpected outcome presents an exciting opening. It beckons further inquiry into why the teaching methodology yielded disparate impacts, which could eventually result in more tailored and effective educational strategies.

In the realm of scientific discoveries, the significance of unexpected results cannot be overstated. Alexander Fleming’s accidental discovery of penicillin originated from what appeared to be a ‘failed’ experiment, but it revolutionised medicine. Similarly, the unintended discovery of cosmic microwave background radiation provided pivotal support for the Big Bang theory. In both instances, what seemed like anomalies paved the way for transformative understanding.

The first task when you encounter unexpected findings is to set them apart from the expected outcomes clearly. Delineate a specific section in your discussion chapter to delve into these anomalies, affording them the attention they merit.

Next, engage in hypothesising why these peculiarities emerged. This could be the point where your years of study and your depth of understanding of your subject really shine. Are there confounding variables that weren’t initially apparent? Could there be an entirely unexplored underlying mechanism at play? Take your reader on this exploration with you, and offer educated guesses based on your literature review and study design.

Lastly, don’t forget to consider and discuss the wider implications of these findings. Could they potentially refute longstanding theories or present the need for a shift in the prevailing school of thought? Or perhaps they hint at previously unthought-of applications or solutions to existing problems? Reflect on how these unexpected results might fit into the broader academic conversation and where future research might take these findings.

By earnestly and transparently tackling unexpected results, you exhibit a commitment to rigorous academic research. The willingness to entertain complexity and to follow the research—even when it leads in unpredictable directions—is a mark of scholarly integrity and courage. This holds true irrespective of your academic discipline, from the humanities and social sciences to STEM fields.

Answering the “so what?” Question

 In your findings chapter you would have presented the data. In the discussion chapter, you answer the ‘so what’ question. Make sure to address it explicitly. Why does your research matter? Who benefits from it? How does it advance the scholarly discourse?

 As a PhD student, you’ve already invested a substantial amount of time and effort into your research. Therefore, it’s crucial to articulate its importance not only to validate your own work but also to contribute meaningfully to your field and, in some cases, to society at large.

 Answering the “so what?” question means connecting the dots between your isolated research findings and the larger intellectual landscape. It requires you to extend your analysis beyond the specifics of your study, considering how it advances the scholarly discourse in your field. For instance, if your research closes a significant gap in the literature, makes a theoretical breakthrough,

Example in Public Health : If your research finds that community-led sanitation programs are far more effective than government-implemented ones, then the “So What?” is clear: policy-makers need to see this data. But that doesn’t mean you don’t still need to discuss it.

Example in Literature : If your research uncovers previously unnoticed patterns of symbolism in 19th-century Russian literature, the “So What?” could be a deeper understanding of how literature reflects societal anxieties of the time.

In order to make your discussion chapter compelling and relevant, it’s imperative to always highlight why your research matters. This goes beyond simply reiterating your findings; you need to connect the dots and show how your research fits into the broader academic landscape. Are you challenging existing theories, confirming previous studies, or offering a new perspective? Establishing the academic importance of your work provides a solid footing for its wider application.

Further to establishing academic relevance, also aim to illuminate the real-world implications of your findings. What are the practical outcomes that could arise from your research? Are there specific scenarios or applications where your research could be a game-changer? For instance, if your study uncovers a more effective method of teaching reading to children with dyslexia, explicitly mention how this could revolutionise educational approaches and improve quality of life for those affected. Providing concrete scenarios enhances the applicability of your research, proving that it doesn’t merely exist in the realm of academic abstraction, but has tangible impacts that can affect change.

Limitations and Future Research

 The quest for perfection is more a journey than a destination. This especially holds true in the context of a PhD thesis. Therefore, a well-crafted Discussion chapter should include a section devoted to the limitations of your research, as it establishes the scope, reliability, and validity of your work. Acknowledging limitations is not an act of undermining your research; instead, it embodies scholarly integrity and rigorous academic thinking.

Being upfront about limitations is essentially about being honest, not only with your readers but also with yourself as a researcher. For instance, if you’ve conducted a survey-based study in psychology but only managed to collect a small number of responses, admitting this limitation provides context for your findings. Perhaps the conclusions drawn from such a sample size are not universally applicable but could still offer significant insights into a particular demographic or condition

• Do not shy away from discussing limitations in fear that it might weaken your arguments.
• Clearly delineate the scope of your research, specifying what it does and doesn’t address.

For example, in a medical research study, if your sample size predominantly consists of individuals from a particular age group, admitting this limitation helps frame your research within that context. Or, if you’re a literature student, if your analysis focuses solely on the works of a single author, your findings might not be generalisable to broader literary trends.

Discussing limitations openly doesn’t devalue your work; it adds a layer of trustworthiness. It assures the reader—and the academic community at large—that you have a nuanced understanding of your research context. It demonstrates that you can critically evaluate your own work, a skill that is paramount.

Your PhD Thesis. On one page.

Example outline for a discussion chapter:.

I’ve included a suggested outline for a PhD discussion chapter. It’s important to note that no two PhDs are alike, and yours may well (probably will) diverge from this. The purpose here is to show how all the various factors we’ve discussed above fit together.

Introduction

• Brief Overview of Research Objectives and Key Findings
• Purpose of the Discussion Chapter

Summary of Key Findings

• Brief Restatement of Research Findings
• Comparison with Initial Hypotheses or Research Questions

Interpretation of Findings

• Contextualisation of Results
• Significance and Implications of the Findings

Evaluation of Existing Theories and Models

• How Your Findings Support or Challenge Previous Work
• Conceptual Contributions of Your Study
• Acknowledgment of Study Limitations
• Suggestions for Future Research
• Summation of Key Points
• Broader Implications and Contributions of the Research
• Final Thoughts and Future Directions

Once you’ve navigated through the complexities of your PhD research, you’re now faced with the challenge of bringing it all together in your discussion chapter. While you’ve already considered various facets like summarising findings, evaluating existing theories, and acknowledging limitations, there are some “easy wins”—small, yet impactful steps—that can help strengthen this critical chapter.

The Power of a Well-Structured Narrative

Begin with a well-structured narrative that clearly outlines your arguments. Tell the reader what the destination is at the outset of the chapter, and then make sure each paragraph is a stepping stone to that destination.

Each paragraph should serve a purpose and should logically follow the previous one. This helps in making your discussion coherent and easy to follow.

• Use transition sentences between paragraphs to guide the reader through your argument.
• Make sure each paragraph adds a new dimension to your discussion.

Data Visualisation Tools

Visual aids aren’t just for presentations; they can provide tremendous value in a discussion chapter. Diagrams, charts, or graphs can provide a visual break and help to emphasise your points effectively.

• Use graphs or charts to represent trends that support your argument.
• Always caption your visuals and reference them in the text.

Integrate Feedback Actively

It’s often beneficial to have colleagues, advisors, or other experts review your discussion section before finalising it. They can offer fresh perspectives and may catch gaps or ambiguities that you’ve missed.

• Seek feedback but also know when to filter it, sticking to advice that genuinely enhances your work.
• Don’t wait until the last minute for feedback; give reviewers ample time.

Highlight the Broader Implications

While you’ll delve into this more in your conclusion, don’t shy away from previewing the broader implications of your work in your discussion. Make it clear why your research matters in a wider context.

• State the broader implications but keep them tightly related to your research findings.
• Avoid making grand claims that your research can’t support

In the journey toward a PhD, learning ‘how to write like a doctor’ is more than mastering grammar or honing your prose; it’s about flexing your academic muscles with confidence and authority. It is in the discussion chapter that you really start flexing, and which you really can and need to speak like a doctor.

For many, this is the first instance of challenging the hegemony of existing literature, refuting established theories, or proposing innovative frameworks. It’s an intimidating task; after all, these are the ideas and research paradigms you’ve been learning about throughout your educational journey. Suddenly, you’re not just absorbing knowledge; you’re contributing to it, critiquing it, and perhaps even changing its trajectory. If it feels challenging, remember that’s because it’s new, and that’s why it’s hard. However, you’ve made it this far, and that alone testifies to your academic rigour and capability. You’ve earned the right to be heard; now it’s time to speak with the academic authority that has been years in the making. So, don’t hold back—flex those academic muscles and carve your niche in the scholarly conversation.

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How Tech Giants Cut Corners to Harvest Data for A.I.

OpenAI, Google and Meta ignored corporate policies, altered their own rules and discussed skirting copyright law as they sought online information to train their newest artificial intelligence systems.

Researchers at OpenAI’s office in San Francisco developed a tool to transcribe YouTube videos to amass conversational text for A.I. development. Credit... Jason Henry for The New York Times

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By Cade Metz ,  Cecilia Kang ,  Sheera Frenkel ,  Stuart A. Thompson and Nico Grant

Reporting from San Francisco, Washington and New York

• Published April 6, 2024 Updated April 8, 2024

In late 2021, OpenAI faced a supply problem.

The artificial intelligence lab had exhausted every reservoir of reputable English-language text on the internet as it developed its latest A.I. system. It needed more data to train the next version of its technology — lots more.

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So OpenAI researchers created a speech recognition tool called Whisper. It could transcribe the audio from YouTube videos, yielding new conversational text that would make an A.I. system smarter.

Some OpenAI employees discussed how such a move might go against YouTube’s rules, three people with knowledge of the conversations said. YouTube, which is owned by Google, prohibits use of its videos for applications that are “independent” of the video platform.

Ultimately, an OpenAI team transcribed more than one million hours of YouTube videos, the people said. The team included Greg Brockman, OpenAI’s president, who personally helped collect the videos, two of the people said. The texts were then fed into a system called GPT-4 , which was widely considered one of the world’s most powerful A.I. models and was the basis of the latest version of the ChatGPT chatbot.

The race to lead A.I. has become a desperate hunt for the digital data needed to advance the technology. To obtain that data, tech companies including OpenAI, Google and Meta have cut corners, ignored corporate policies and debated bending the law, according to an examination by The New York Times.

At Meta, which owns Facebook and Instagram, managers, lawyers and engineers last year discussed buying the publishing house Simon & Schuster to procure long works, according to recordings of internal meetings obtained by The Times. They also conferred on gathering copyrighted data from across the internet, even if that meant facing lawsuits. Negotiating licenses with publishers, artists, musicians and the news industry would take too long, they said.

Like OpenAI, Google transcribed YouTube videos to harvest text for its A.I. models, five people with knowledge of the company’s practices said. That potentially violated the copyrights to the videos, which belong to their creators.

Last year, Google also broadened its terms of service. One motivation for the change, according to members of the company’s privacy team and an internal message viewed by The Times, was to allow Google to be able to tap publicly available Google Docs, restaurant reviews on Google Maps and other online material for more of its A.I. products.

The companies’ actions illustrate how online information — news stories, fictional works, message board posts, Wikipedia articles, computer programs, photos, podcasts and movie clips — has increasingly become the lifeblood of the booming A.I. industry. Creating innovative systems depends on having enough data to teach the technologies to instantly produce text, images, sounds and videos that resemble what a human creates.

The volume of data is crucial. Leading chatbot systems have learned from pools of digital text spanning as many as three trillion words, or roughly twice the number of words stored in Oxford University’s Bodleian Library, which has collected manuscripts since 1602. The most prized data, A.I. researchers said, is high-quality information, such as published books and articles, which have been carefully written and edited by professionals.

For years, the internet — with sites like Wikipedia and Reddit — was a seemingly endless source of data. But as A.I. advanced, tech companies sought more repositories. Google and Meta, which have billions of users who produce search queries and social media posts every day, were largely limited by privacy laws and their own policies from drawing on much of that content for A.I.

Their situation is urgent. Tech companies could run through the high-quality data on the internet as soon as 2026, according to Epoch, a research institute. The companies are using the data faster than it is being produced.

“The only practical way for these tools to exist is if they can be trained on massive amounts of data without having to license that data,” Sy Damle, a lawyer who represents Andreessen Horowitz, a Silicon Valley venture capital firm, said of A.I. models last year in a public discussion about copyright law. “The data needed is so massive that even collective licensing really can’t work.”

Tech companies are so hungry for new data that some are developing “synthetic” information. This is not organic data created by humans, but text, images and code that A.I. models produce — in other words, the systems learn from what they themselves generate.

OpenAI said each of its A.I. models “has a unique data set that we curate to help their understanding of the world and remain globally competitive in research.” Google said that its A.I. models “are trained on some YouTube content,” which was allowed under agreements with YouTube creators, and that the company did not use data from office apps outside of an experimental program. Meta said it had “made aggressive investments” to integrate A.I. into its services and had billions of publicly shared images and videos from Instagram and Facebook for training its models.

For creators, the growing use of their works by A.I. companies has prompted lawsuits over copyright and licensing. The Times sued OpenAI and Microsoft last year for using copyrighted news articles without permission to train A.I. chatbots. OpenAI and Microsoft have said using the articles was “fair use,” or allowed under copyright law, because they transformed the works for a different purpose.

More than 10,000 trade groups, authors, companies and others submitted comments last year about the use of creative works by A.I. models to the Copyright Office , a federal agency that is preparing guidance on how copyright law applies in the A.I. era.

Justine Bateman, a filmmaker, former actress and author of two books, told the Copyright Office that A.I. models were taking content — including her writing and films — without permission or payment.

“This is the largest theft in the United States, period,” she said in an interview.

‘Scale Is All You Need’

In January 2020, Jared Kaplan, a theoretical physicist at Johns Hopkins University, published a groundbreaking paper on A.I. that stoked the appetite for online data.

His conclusion was unequivocal: The more data there was to train a large language model — the technology that drives online chatbots — the better it would perform. Just as a student learns more by reading more books, large language models can better pinpoint patterns in text and be more accurate with more information.

“Everyone was very surprised that these trends — these scaling laws as we call them — were basically as precise as what you see in astronomy or physics,” said Dr. Kaplan, who published the paper with nine OpenAI researchers. (He now works at the A.I. start-up Anthropic.)

“Scale is all you need” soon became a rallying cry for A.I.

Researchers have long used large public databases of digital information to develop A.I., including Wikipedia and Common Crawl, a database of more than 250 billion web pages collected since 2007. Researchers often “cleaned” the data by removing hate speech and other unwanted text before using it to train A.I. models.

In 2020, data sets were tiny by today’s standards. One database containing 30,000 photographs from the photo website Flickr was considered a vital resource at the time.

After Dr. Kaplan’s paper, that amount of data was no longer enough. It became all about “just making things really big,” said Brandon Duderstadt, the chief executive of Nomic, an A.I. company in New York.

Before 2020, most A.I. models used relatively little training data.

Mr. Kaplan’s paper, released in 2020, led to a new era defined by GPT-3, a large language model, where researchers began including more data in their models …

… much, much more data.

When OpenAI unveiled GPT-3 in November 2020, it was trained on the largest amount of data to date — about 300 billion “tokens,” which are essentially words or pieces of words. After learning from that data, the system generated text with astounding accuracy, writing blog posts, poetry and its own computer programs.

In 2022, DeepMind, an A.I. lab owned by Google, went further. It tested 400 A.I. models and varied the amount of training data and other factors. The top-performing models used even more data than Dr. Kaplan had predicted in his paper. One model, Chinchilla, was trained on 1.4 trillion tokens.

It was soon overtaken. Last year, researchers from China released an A.I. model, Skywork , which was trained on 3.2 trillion tokens from English and Chinese texts. Google also unveiled an A.I. system, PaLM 2 , which topped 3.6 trillion tokens .

Transcribing YouTube

In May, Sam Altman , the chief executive of OpenAI, acknowledged that A.I. companies would use up all viable data on the internet.

“That will run out,” he said in a speech at a tech conference.

Mr. Altman had seen the phenomenon up close. At OpenAI, researchers had gathered data for years, cleaned it and fed it into a vast pool of text to train the company’s language models. They had mined the computer code repository GitHub, vacuumed up databases of chess moves and drawn on data describing high school tests and homework assignments from the website Quizlet.

By late 2021, those supplies were depleted, said eight people with knowledge of the company, who were not authorized to speak publicly.

OpenAI was desperate for more data to develop its next-generation A.I. model, GPT-4. So employees discussed transcribing podcasts, audiobooks and YouTube videos, the people said. They talked about creating data from scratch with A.I. systems. They also considered buying start-ups that had collected large amounts of digital data.

OpenAI eventually made Whisper, the speech recognition tool, to transcribe YouTube videos and podcasts, six people said. But YouTube prohibits people from not only using its videos for “independent” applications, but also accessing its videos by “any automated means (such as robots, botnets or scrapers).”

OpenAI employees knew they were wading into a legal gray area, the people said, but believed that training A.I. with the videos was fair use. Mr. Brockman, OpenAI’s president, was listed in a research paper as a creator of Whisper. He personally helped gather YouTube videos and fed them into the technology, two people said.

Mr. Brockman referred requests for comment to OpenAI, which said it uses “numerous sources” of data.

Last year, OpenAI released GPT-4, which drew on the more than one million hours of YouTube videos that Whisper had transcribed. Mr. Brockman led the team that developed GPT-4.

Some Google employees were aware that OpenAI had harvested YouTube videos for data, two people with knowledge of the companies said. But they didn’t stop OpenAI because Google had also used transcripts of YouTube videos to train its A.I. models, the people said. That practice may have violated the copyrights of YouTube creators. So if Google made a fuss about OpenAI, there might be a public outcry against its own methods, the people said.

Matt Bryant, a Google spokesman, said the company had no knowledge of OpenAI’s practices and prohibited “unauthorized scraping or downloading of YouTube content.” Google takes action when it has a clear legal or technical basis to do so, he said.

Google’s rules allowed it to tap YouTube user data to develop new features for the video platform. But it was unclear whether Google could use YouTube data to build a commercial service beyond the video platform, such as a chatbot.

Geoffrey Lottenberg, an intellectual property lawyer with the law firm Berger Singerman, said Google’s language about what it could and could not do with YouTube video transcripts was vague.

“Whether the data could be used for a new commercial service is open to interpretation and could be litigated,” he said.

In late 2022, after OpenAI released ChatGPT and set off an industrywide race to catch up , Google researchers and engineers discussed tapping other user data. Billions of words sat in people’s Google Docs and other free Google apps. But the company’s privacy restrictions limited how they could use the data, three people with knowledge of Google’s practices said.

In June, Google’s legal department asked the privacy team to draft language to broaden what the company could use consumer data for, according to two members of the privacy team and an internal message viewed by The Times.

The employees were told Google wanted to use people’s publicly available content in Google Docs, Google Sheets and related apps for an array of A.I. products. The employees said they didn’t know if the company had previously trained A.I. on such data.

At the time, Google’s privacy policy said the company could use publicly available information only to “help train Google’s language models and build features like Google Translate.”

The privacy team wrote new terms so Google could tap the data for its “A.I. models and build products and features like Google Translate, Bard and Cloud AI capabilities,” which was a wider collection of A.I. technologies.

“What is the end goal here?” one member of the privacy team asked in an internal message. “How broad are we going?”

The team was told specifically to release the new terms on the Fourth of July weekend, when people were typically focused on the holiday, the employees said. The revised policy debuted on July 1, at the start of the long weekend.

How Google Can Use Your Data

Here are the changes Google made to its privacy policy last year for its free consumer apps.

Google uses information to improve our services and to develop new products, features and technologies that benefit our users and the public. For example, we use publicly available information to help train Google’s language AI models and build products and features like Google Translate , Bard, and Cloud AI capabilities .

In August, two privacy team members said, they pressed managers on whether Google could start using data from free consumer versions of Google Docs, Google Sheets and Google Slides. They were not given clear answers, they said.

Mr. Bryant said that the privacy policy changes had been made for clarity and that Google did not use information from Google Docs or related apps to train language models “without explicit permission” from users, referring to a voluntary program that allows users to test experimental features.

“We did not start training on additional types of data based on this language change,” he said.

The Debate at Meta

Mark Zuckerberg, Meta’s chief executive, had invested in A.I. for years — but suddenly found himself behind when OpenAI released ChatGPT in 2022. He immediately pushed to match and exceed ChatGPT , calling executives and engineers at all hours of the night to push them to develop a rival chatbot, said three current and former employees, who were not authorized to discuss confidential conversations.

But by early last year, Meta had hit the same hurdle as its rivals: not enough data.

Ahmad Al-Dahle, Meta’s vice president of generative A.I., told executives that his team had used almost every available English-language book, essay, poem and news article on the internet to develop a model, according to recordings of internal meetings, which were shared by an employee.

Meta could not match ChatGPT unless it got more data, Mr. Al-Dahle told colleagues. In March and April 2023, some of the company’s business development leaders, engineers and lawyers met nearly daily to tackle the problem.

Some debated paying $10 a book for the full licensing rights to new titles. They discussed buying Simon & Schuster, which publishes authors like Stephen King, according to the recordings.

They also talked about how they had summarized books, essays and other works from the internet without permission and discussed sucking up more, even if that meant facing lawsuits. One lawyer warned of “ethical” concerns around taking intellectual property from artists but was met with silence, according to the recordings.

Mr. Zuckerberg demanded a solution, employees said.

“The capability that Mark is looking for in the product is just something that we currently aren’t able to deliver,” one engineer said.

While Meta operates giant social networks, it didn’t have troves of user posts at its disposal, two employees said. Many Facebook users had deleted their earlier posts, and the platform wasn’t where people wrote essay-type content, they said.

Meta was also limited by privacy changes it introduced after a 2018 scandal over sharing its users’ data with Cambridge Analytica, a voter-profiling company.

Mr. Zuckerberg said in a recent investor call that the billions of publicly shared videos and photos on Facebook and Instagram are “greater than the Common Crawl data set.”

During their recorded discussions, Meta executives talked about how they had hired contractors in Africa to aggregate summaries of fiction and nonfiction. The summaries included copyrighted content “because we have no way of not collecting that,” a manager said in one meeting.

Meta’s executives said OpenAI seemed to have used copyrighted material without permission. It would take Meta too long to negotiate licenses with publishers, artists, musicians and the news industry, they said, according to the recordings.

“The only thing that’s holding us back from being as good as ChatGPT is literally just data volume,” Nick Grudin, a vice president of global partnership and content, said in one meeting.

OpenAI appeared to be taking copyrighted material and Meta could follow this “market precedent,” he added.

Meta’s executives agreed to lean on a 2015 court decision involving the Authors Guild versus Google , according to the recordings. In that case, Google was permitted to scan, digitize and catalog books in an online database after arguing that it had reproduced only snippets of the works online and had transformed the originals, which made it fair use.

Using data to train A.I. systems, Meta’s lawyers said in their meetings, should similarly be fair use.

At least two employees raised concerns about using intellectual property and not paying authors and other artists fairly or at all, according to the recordings. One employee recounted a separate discussion about copyrighted data with senior executives including Chris Cox, Meta’s chief product officer, and said no one in that meeting considered the ethics of using people’s creative works.

‘Synthetic’ Data

OpenAI’s Mr. Altman had a plan to deal with the looming data shortage.

Companies like his, he said at the May conference, would eventually train their A.I. on text generated by A.I. — otherwise known as synthetic data.

Since an A.I. model can produce humanlike text, Mr. Altman and others have argued, the systems can create additional data to develop better versions of themselves. This would help developers build increasingly powerful technology and reduce their dependence on copyrighted data.

“As long as you can get over the synthetic data event horizon, where the model is smart enough to make good synthetic data, everything will be fine,” Mr. Altman said.

A.I. researchers have explored synthetic data for years. But building an A.I system that can train itself is easier said than done. A.I. models that learn from their own outputs can get caught in a loop where they reinforce their own quirks, mistakes and limitations.

“The data these systems need is like a path through the jungle,” said Jeff Clune, a former OpenAI researcher who now teaches computer science at the University of British Columbia. “If they only train on synthetic data, they can get lost in the jungle.”

To combat this, OpenAI and others are investigating how two different A.I. models might work together to generate synthetic data that is more useful and reliable. One system produces the data, while a second judges the information to separate the good from the bad. Researchers are divided on whether this method will work.

A.I. executives are barreling ahead nonetheless.

“It should be all right,” Mr. Altman said at the conference.

Read by Cade Metz

Audio produced by Patricia Sulbarán .

An earlier version of this article misstated the publisher of J.K. Rowling’s books. Her works have been published by Scholastic, Little, Brown and others. They were not published by Simon & Schuster.

How we handle corrections

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

Cecilia Kang reports on technology and regulatory policy and is based in Washington D.C. She has written about technology for over two decades. More about Cecilia Kang

Sheera Frenkel is a reporter based in the San Francisco Bay Area, covering the ways technology impacts everyday lives with a focus on social media companies, including Facebook, Instagram, Twitter, TikTok, YouTube, Telegram and WhatsApp. More about Sheera Frenkel

Stuart A. Thompson writes about how false and misleading information spreads online and how it affects people around the world. He focuses on misinformation, disinformation and other misleading content. More about Stuart A. Thompson

Nico Grant is a technology reporter covering Google from San Francisco. Previously, he spent five years at Bloomberg News, where he focused on Google and cloud computing. More about Nico Grant

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