hypothesis generator biology

Hypothesis Maker Online

Looking for a hypothesis maker? This online tool for students will help you formulate a beautiful hypothesis quickly, efficiently, and for free.

Are you looking for an effective hypothesis maker online? Worry no more; try our online tool for students and formulate your hypothesis within no time.

• 🔎 How to Use the Tool?
• ⚗️ What Is a Hypothesis in Science?

👍 What Does a Good Hypothesis Mean?

• 🧭 Steps to Making a Good Hypothesis

🔗 References

📄 hypothesis maker: how to use it.

Our hypothesis maker is a simple and efficient tool you can access online for free.

If you want to create a research hypothesis quickly, you should fill out the research details in the given fields on the hypothesis generator.

Below are the fields you should complete to generate your hypothesis:

• Who or what is your research based on? For instance, the subject can be research group 1.
• What does the subject (research group 1) do?
• What does the subject affect? - This shows the predicted outcome, which is the object.
• Who or what will be compared with research group 1? (research group 2).

Once you fill the in the fields, you can click the ‘Make a hypothesis’ tab and get your results.

⚗️ What Is a Hypothesis in the Scientific Method?

A hypothesis is a statement describing an expectation or prediction of your research through observation.

It is similar to academic speculation and reasoning that discloses the outcome of your scientific test . An effective hypothesis, therefore, should be crafted carefully and with precision.

A good hypothesis should have dependent and independent variables . These variables are the elements you will test in your research method – it can be a concept, an event, or an object as long as it is observable.

You can observe the dependent variables while the independent variables keep changing during the experiment.

In a nutshell, a hypothesis directs and organizes the research methods you will use, forming a large section of research paper writing.

Hypothesis vs. Theory

A hypothesis is a realistic expectation that researchers make before any investigation. It is formulated and tested to prove whether the statement is true. A theory, on the other hand, is a factual principle supported by evidence. Thus, a theory is more fact-backed compared to a hypothesis.

Another difference is that a hypothesis is presented as a single statement , while a theory can be an assortment of things . Hypotheses are based on future possibilities toward a specific projection, but the results are uncertain. Theories are verified with undisputable results because of proper substantiation.

When it comes to data, a hypothesis relies on limited information , while a theory is established on an extensive data set tested on various conditions.

You should observe the stated assumption to prove its accuracy.

Since hypotheses have observable variables, their outcome is usually based on a specific occurrence. Conversely, theories are grounded on a general principle involving multiple experiments and research tests.

This general principle can apply to many specific cases.

The primary purpose of formulating a hypothesis is to present a tentative prediction for researchers to explore further through tests and observations. Theories, in their turn, aim to explain plausible occurrences in the form of a scientific study.

It would help to rely on several criteria to establish a good hypothesis. Below are the parameters you should use to analyze the quality of your hypothesis.

🧭 6 Steps to Making a Good Hypothesis

Writing a hypothesis becomes way simpler if you follow a tried-and-tested algorithm. Let’s explore how you can formulate a good hypothesis in a few steps:

Step #1: Ask Questions

The first step in hypothesis creation is asking real questions about the surrounding reality.

Why do things happen as they do? What are the causes of some occurrences?

Your curiosity will trigger great questions that you can use to formulate a stellar hypothesis. So, ensure you pick a research topic of interest to scrutinize the world’s phenomena, processes, and events.

Step #2: Do Initial Research

Carry out preliminary research and gather essential background information about your topic of choice.

The extent of the information you collect will depend on what you want to prove.

Your initial research can be complete with a few academic books or a simple Internet search for quick answers with relevant statistics.

Still, keep in mind that in this phase, it is too early to prove or disapprove of your hypothesis.

Step #3: Identify Your Variables

Now that you have a basic understanding of the topic, choose the dependent and independent variables.

Take note that independent variables are the ones you can’t control, so understand the limitations of your test before settling on a final hypothesis.

Step #4: Formulate Your Hypothesis

You can write your hypothesis as an ‘if – then’ expression . Presenting any hypothesis in this format is reliable since it describes the cause-and-effect you want to test.

For instance: If I study every day, then I will get good grades.

Step #5: Gather Relevant Data

Once you have identified your variables and formulated the hypothesis, you can start the experiment. Remember, the conclusion you make will be a proof or rebuttal of your initial assumption.

So, gather relevant information, whether for a simple or statistical hypothesis, because you need to back your statement.

Step #6: Record Your Findings

Finally, write down your conclusions in a research paper .

Outline in detail whether the test has proved or disproved your hypothesis.

Edit and proofread your work, using a plagiarism checker to ensure the authenticity of your text.

We hope that the above tips will be useful for you. Note that if you need to conduct business analysis, you can use the free templates we’ve prepared: SWOT , PESTLE , VRIO , SOAR , and Porter’s 5 Forces .

❓ Hypothesis Formulator FAQ

Updated: Oct 25th, 2023

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Use our hypothesis maker whenever you need to formulate a hypothesis for your study. We offer a very simple tool where you just need to provide basic info about your variables, subjects, and predicted outcomes. The rest is on us. Get a perfect hypothesis in no time!

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AI Generators in Science and Research

🔬✍️ Formulate precise, well-founded hypotheses for your studies and scientific work. Explore the potential of your research!

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Discover the power of a well-formulated hypothesis with our Research Hypothesis Generator. In the world of scientific research, a solid, relevant hypothesis is the foundation on which any study is built.

🧪 Structured and precise

A well-defined hypothesis can guide your experiments and set the course for your discoveries. Our generator provides you with structured proposals based on your field and subject.

🌌 For all areas

Whether you're in biology, physics or the social sciences, we've got you covered. adapted our tool to meet the diversity of research needs.

💭 Refine Your Thinking

With our help, crystallize your idea into a clear, logical hypothesis. Each proposal is designed to stimulate your thinking and enrich your scientific approach.

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Hypothesis Generator

Generate hypotheses for your research.

• Academic Research: Generate hypotheses for your thesis, dissertation, or any academic paper.
• Data Analysis: Create hypotheses to guide your data exploration and analysis.
• Market Research: Develop hypotheses to understand market trends and consumer behavior.
• Product Development: Formulate hypotheses to guide your product testing and development process.
• Scientific Research: Generate hypotheses for your experiments or observational studies.

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Writing style mimic, perspective diversifier, teenspeak topic explainer.

Use Our Free A/B Testing Hypothesis Generator . Never Miss Key Elements From Your Hypotheses. Get Big Conversion Lifts.

Observation, inadvertent impact.

Streamline Your Hypothesis Generation Research with Custom Templates the Pros Use.

Have questions about a/b testing hypotheses, what is a hypothesis.

Many people define a hypothesis as an “educated guess”.

To be more precise, a properly constructed hypothesis predicts a possible outcome to an experiment or a test where one variable (the independent one ) is tweaked and/or modified and the impact is measured by the change in behavior of another variable (generally the dependent one).

A hypothesis should be specific (it should clearly define what is being altered and what is the expected impact), data-driven (the changes being made to the independent variable should be based on historic data or theories that have been proven in the past), and testable (it should be possible to conduct the proposed test in a controlled environment to establish the relationship between the variables involved, and disprove the hypothesis - should it be untrue.)

What is the Cost of a Hastily Assembled Hypothesis?

According to an analysis of over 28,000 tests run using the Convert Experiences platform, only 1 in 5 tests proves to be statistically significant.

While more and more debate is opening up around sticking to the concept of 95% statistical significance, it is still a valid rule of thumb for optimizers who do not want to get into the fray with peeking vs. no peeking, and custom stopping rules for experiments.

There might be a multitude of reasons why a test does not reach statistical significance. But framing a tenable hypothesis that already proves itself logistically feasible on paper is a better starting point than a hastily assembled assumption.

Moreover, the aim of an A/B test may be to extract a learning, but some learnings come with heavy costs. 26% decrease in conversion rates to be specific.

A robust hypothesis may not be the answer to all testing woes, but it does help prioritisation of possible solutions and leads testing teams to pick low hanging fruits.

How is an A/B Testing Hypothesis Different?

An A/B test should be treated with the same rigour as tests conducted in laboratories. That is an easy way to guarantee better hypotheses, more relevant experiments, and ultimately more profitable optimization programs.

The focus of an A/B test should be on first extracting a learning , and then monetizing it in the form of increased registration completions, better cart conversions and more revenue.

If that is true, then an A/B test hypothesis is not very different from a regular scientific hypothesis. With a couple of interesting points to note:

• Most scientific hypotheses proceed with one independent variable and one dependent variable, for the sake of simplicity. But in A/B tests, there might be changes made to several independent variables at the same time. Under such circumstances it is good to explore the relationship between the independent variables to make sure that they do not inadvertently impact one another. For example changing both the value proposition and button copy of a landing page to determine improvement in click through or completion rates is tricky. Reaching a point where the browser is compelled to click the button could easily have been impacted by the value proposition (as in a strong hook and heading). So what caused the improvement in the dependent variable? Was it the change to the first element or the second one?
• The concept of Operational Definition is non-negotiable in most laboratory experiments. And comes baked with the question of ethics or morality. Operation Definition is the specific process that will be used to quantify the change in the value/behavior of the independent variable in the test. As an example, if a test wishes to measure the level of frustration that subjects experience when they are exposed to certain stimuli, researchers must be careful to define exactly how they will measure the output or frustration. Should they allow the test subjects to act out, in which case they may hurt or harm other individuals. Or should they use a non-invasive technique like an fMRI scan to monitor brain activity and collect the needed data. In A/B tests however, since data is collected through relatively inanimate channels like analytics dashboards, generally little thought is spared to Operational Definition and the impact of A/B testing on the human subjects (site traffic in this case).

The 5 Essential Parts of an A/B Testing Hypothesis

A robust A/B testing hypothesis should be assembled in 5 key parts:

1. OBSERVATION

This includes a clear outline of the problem (the unexplained phenomenon) observed and what it entails. This section should be completely free of conjecture and rely solely on good quality data - either qualitative and/or quantitative - to bring a potential area of improvement to light. It also includes a mention of the way in which the data is collected.

Proper observation ensures a credible hypothesis that is easy to “defend” later down the line.

2. EXECUTION

This is the where, what, and the who of the A/B test. It specifies the change(s) you will be making to site element(s) in an attempt to solve the problem that has been outlined under “OBSERVATION”. It serves to also clearly define the segment of site traffic that will be exposed to the experiment.

Proper execution guidelines set the rhythm for the A/B test. They define how easy or difficult it will be to deploy the test and thus aid hypothesis prioritization .

This is where you make your educated guess or informed prediction. Based on a diligently identified OBSERVATION and EXECUTION guidelines that are possible to deploy, your OUTCOME should clearly mention two things:

• The change (increase or decrease) you expect to see to the problem or the symptoms of the problem identified under OBSERVATION.
• The Key Performance Indicators (KPIs) you will be monitoring to gauge whether your prediction has panned out, or not.

In general most A/B tests have one primary KPI and a couple of secondary KPIs or ways to measure impact. This is to ensure that external influences do not skew A/B test results and even if the primary KPI is compromised in some way, the secondary KPIs do a good job of indicating that the change is indeed due to the implementation of the EXECUTION guidelines, and not the result of unmonitored external factors.

4. LOGISTICS

An important part of hypothesis formulation, LOGISTICS talk about what it will take to collect enough clean data from which a reliable conclusion can be drawn. How many unique tested visitors, what is the statistical significance desired, how many conversions is enough and what is the duration for which the A/B test should run? Each question on its own merits a blog or a lesson. But for the sake of convenience, Convert has created a Free Sample Size & A/B/N Test Duration Calculator .

Set the right logistical expectations so that you can prioritise your hypotheses for maximum impact and minimum effort .

5. INADVERTENT IMPACT

This is a nod in the direction of ethics in A/B testing and marketing, because experiments involve humans and optimizers should be aware of the possible impact on their behavior.

Often a thorough analysis at this stage can modify the way impact is measured or an experiment is conducted. Or Convert certainly hopes that this will be the case in future. Here’s why ethics do matter in testing.

Now Organize, Prioritise & Learn from Your Hypotheses.

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Online Hypothesis Generator

Add the required information into the fields below to build a list of well-formulated hypotheses.

• If patients follow medical prescriptions, then their condition will improve.
• If patients follow medical prescriptions, then their condition will show better results.
• If patients follow medical prescriptions, then their condition will show better results than those who do not follow medical prescriptions.
• H0 (null hypothesis) - Attending most lectures by first-year students has no effect on their exam scores.
• H1 (alternative hypothesis) - Attending most lectures by first-year students has a positive effect on their exam scores.

* Hint - choose either null or alternative hypothesis

⭐️ Hypothesis Creator: the Benefits

• 🔎 How to Use the Tool?
• 🤔 What Is a Hypothesis?
• 👣 Steps to Generating a Hypothesis
• 🔍 References

🔎 Hypothesis Generator: How to Use It?

The generation of a workable hypothesis is not an easy task for many students. You need to research widely, understand the gaps in your study area, and comprehend the method of hypothesis formulation to the dot. Lucky for you, we have a handy hypothesis generator that takes hours of tedious work out of your study process.

To use our hypothesis generator, you’ll need to do the following:

• Indicate your experimental group (people, phenomena, event)
• Stipulate what it does
• Add the effect that the subject’s activities produce
• Specify the comparison group

Once you put all this data into our online hypothesis generator, click on the “Generate hypothesis” tab and enjoy instant results. The tool will come up with a well-formulated hypothesis in seconds.

🤔 What Is a Research Hypothesis?

A hypothesis is a claim or statement you make about the assumed relationship between the dependent and independent variables you're planning to test. It is formulated at the beginning of your study to show the direction you will take in the analysis of your subject of interest.

The hypothesis works in tandem with your research purpose and research question , delineating your entire perspective.

For example, if you focus on the quality of palliative care in the USA , your perspective may be as follows.

This way, your hypothesis serves as a tentative answer to your research question, which you aim to prove or disprove with scientific data, statistics, and analysis.

Hypothesis Types

In most scholarly studies, you’ll be required to write hypotheses in pairs – as a null and alternative hypothesis :

• The alternative hypothesis assumes a statistically significant relationship between the identified variables. Thus, if you find that relationship in the analysis process, you can consider the alternative hypothesis proven.
• A null hypothesis is the opposite; it assumes that there is no relationship between the variables. Thus, if you find no statistically significant association, the null hypothesis is considered proven.

A handy example is as follows:

You are researching the impact of sugar intake on child obesity . So, based on your data, you can either find that the number of sugar spoons a day directly impacts obesity or that the sugar intake is not associated with obesity in your sample. The hypotheses for this study would be as follows:

ALTERNATIVE

There is a relationship between the number of sugar spoons consumed daily and obesity in U.S. preschoolers.

There is no relationship between the number of sugar spoons consumed daily and obesity in U.S. preschoolers.

Besides, hypotheses can be directional and non-directional by type:

• A directional hypothesis assumes a cause-and-effect relationship between variables, clearly designating the assumed difference in study groups or parameters.
• A non-directional hypothesis , in turn, only assumes a relationship or difference without a clear estimate of its direction.

NON-DIRECTIONAL

Students in high school and college perform differently on critical thinking tests.

DIRECTIONAL

College students perform better on critical thinking tests that high-school students.

👣 How to Make a Hypothesis in Research

Now let’s cover the algorithm of hypothesis generation to make this process simple and manageable for you.

Step #1: Formulate Your Research Question

The first step is to create a research question . Study the topic of interest and clarify what aspect you're fascinated about, wishing to learn more about the hidden connections, effects, and relationships.

Step #2: Research the Topic

Next, you should conduct some research to test your assumption and see whether there’s enough published evidence to back up your point. You should find credible sources that discuss the concepts you’ve singled out for the study and delineate a relationship between them. Once you identify a reasonable body of research, it’s time to go on.

Step #3: Make an Assumption

With some scholarly data, you should now be better positioned to make a researchable assumption.

For instance, if you find out that many scholars associate heavy social media use with a feeling of loneliness, you can hypothesize that the hours spent on social networks will directly correlate with perceived loneliness intensity.

Step #4: Improve Your Hypothesis

Now that you have a hypothesis, it’s time to refine it by adding context and population specifics. Who will you study? What social network will you focus on? In this example, you can focus on the student sample’s use of Instagram .

Step #5: Try Different Phrasing

The final step is the proper presentation of your hypothesis. You can try several variants, focusing on the variables, correlations , or groups you compare.

For instance, you can say that students spending 3+ hours on Instagram every day are lonelier than their peers. Otherwise, you can hypothesize that heavy social media use leads to elevated feelings of loneliness.

👀 Null Hypothesis Examples

If you’re unsure about how to generate great hypotheses, get some inspiration from the list of examples formulated by our writing pros.

Thank you for reading this article! If you’re planning to analyze business issues, try our free templates: PEST , PESTEL , SWOT , SOAR , VRIO , and Five Forces .

❓ Hypothesis Generator FAQ

❓ what does hypothesis mean.

A hypothesis in an essay or a larger research assignment is your claim or prediction of the relationship you assume between the identified dependent and independent variables. You share an assumption that you’re going to test with research and data analysis in the later sections of your paper.

❓ How to create a hypothesis?

The first step to formulating a good hypothesis is to ask a question about your subject of interest and understand what effects it may experience from external sources or how it changes over time. You can identify differences between groups and inquire into the nature of those distinctions. In any way, you need to voice some assumption that you’ll further test with data; that assumption will be your hypothesis for a study.

❓ What is a null and alternative hypothesis?

You need to formulate a null and alternative hypothesis if you plan to test some relationship between variables with statistical instruments. For example, you might compare a group of students on an emotional intelligence scale to determine whether first-year students are less emotionally competent than graduates. In this case, your alternative hypothesis would state that they are, and a null hypothesis would say that there is no difference between student groups.

❓ What does it mean to reject the null hypothesis?

A null hypothesis assumes that there is no difference between groups or that the dependent variables don't have any sizable impact on the independent variable. If your null hypothesis gets rejected, it means that your alternative hypothesis has been proved, showing that there is a tangible difference or relationship between your variables.

Hypothesis-generating research and predictive medicine

Affiliation.

• 1 National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. [email protected]
• PMID: 23817045
• PMCID: PMC3698497
• DOI: 10.1101/gr.157826.113

Genomics has profoundly changed biology by scaling data acquisition, which has provided researchers with the opportunity to interrogate biology in novel and creative ways. No longer constrained by low-throughput assays, researchers have developed hypothesis-generating approaches to understand the molecular basis of nature-both normal and pathological. The paradigm of hypothesis-generating research does not replace or undermine hypothesis-testing modes of research; instead, it complements them and has facilitated discoveries that may not have been possible with hypothesis-testing research. The hypothesis-generating mode of research has been primarily practiced in basic science but has recently been extended to clinical-translational work as well. Just as in basic science, this approach to research can facilitate insights into human health and disease mechanisms and provide the crucially needed data set of the full spectrum of genotype-phenotype correlations. Finally, the paradigm of hypothesis-generating research is conceptually similar to the underpinning of predictive genomic medicine, which has the potential to shift medicine from a primarily population- or cohort-based activity to one that instead uses individual susceptibility, prognostic, and pharmacogenetic profiles to maximize the efficacy and minimize the iatrogenic effects of medical interventions.

Publication types

• Genomics / trends*
• Medicine / trends*
• Research / trends*
• Translational Research, Biomedical

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Introduction

There is a problem: data from the field, how should hypothesis & prediction be defined, hypothesis generation in biology : a science teaching challenge & potential solution.

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Paul K. Strode; Hypothesis Generation in Biology : A Science Teaching Challenge & Potential Solution . The American Biology Teacher 1 September 2015; 77 (7): 500–506. doi: https://doi.org/10.1525/abt.2015.77.7.4

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Helping students understand and generate appropriate hypotheses and test their subsequent predictions – in science in general and biology in particular – should be at the core of teaching the nature of science. However, there is much confusion among students and teachers about the difference between hypotheses and predictions. Here, I present evidence of the problem and describe steps that scientists actually follow when employing scientific reasoning strategies. This is followed by a proposed solution for helping students effectively explore this important aspect of the nature of science.

I taught high school biology and chemistry for 8 years before beginning a doctoral program in ecology and environmental science at the University of Illinois. Graduate school revealed that, while I had been effective in teaching science content to my students, I had mostly failed in teaching them the nature of science (NOS). Indeed, I had even promoted several of the myths of science outlined by McComas (1996) – most blatantly that “a hypothesis is an educated guess” and “science is procedural more than creative.” I had even failed at understanding and teaching the hypothetico-deductive method of science that so many science teachers (this author included) mislead their students into thinking is the only way to practice science : formulate a hypothesis, deduce its consequences (make a prediction), and observe those consequences (perform an experiment and collect data).

For example, in my second year of graduate school, a chance conversation in the woods with one of my committee members revealed my own shortfalls. When pressed for the hypothesis I was testing with my research, I delivered the prediction that if we had an average spring warm-up, then the timing of leaf growth, caterpillar hatching, and bird migration would be synchronized, but if we had an early or late spring, there would be a mismatch in one or more of the trophic levels. I had given my committee member an “educated guess,” an “If…, then…” statement exactly in the form I had learned in my science classes and identical to how I had taught my high school students to write hypotheses. While I may have based my prediction on some overarching patterns or underlying mechanisms that were already known for the community interactions I was studying, I certainly could not verbalize them.

Since returning to teaching high school biology after graduate school, I work to help my students hone the scientific reasoning strategies of abduction (ingenuity, or borrowing an idea from earlier studies), deduction, and induction. But with such an NOS focus in my classroom on these reasoning skills, I have become somewhat hypersensitive to moments when students get it wrong – for example, when students inappropriately marry a method with the tail end of a deductive statement ( If I do X, then Y will happen ) and call it a “hypothesis.”

Most commonly in scientific research, a hypothesis is a tentative, testable, and falsifiable statement that explains some observed phenomenon in nature. We more specifically call this kind of statement an explanatory hypothesis . However, as we will see, a hypothesis can also be a statement that describes an observed pattern in nature. We call this kind a generalizing hypothesis .

In the sections that follow, I present evidence that students, teachers, textbooks, and even practicing scientists confuse predictions with hypotheses. I then discuss the ways the terms are defined and used in the logical practice of scientific reasoning. Finally, I provide some simple ideas for how we can improve the teaching of NOS in the classroom.

In 2006, I chaperoned a group of high school students presenting precollege research at the Intel International Science and Engineering Fair (Intel ISEF) in Indianapolis. Upon inspection of a wide range of student poster presentations, I observed that several students had written predictions on their posters but labeled them “hypotheses.” In the interest of quantifying this misconception, I quickly designed a small survey and randomly sampled all non-engineering and non-math projects with project numbers ending in 1, 4, or 7 (n = 127). In this initial survey, 78 (80%) of 98 student posters reviewed had incorrectly identified a prediction as a hypothesis.

Where had these students gone wrong or been misled during their formal science education or in their science-fair preparation work? Indeed, it is human nature to formulate explanations for observed natural phenomena ( Brewer et al., 1998 ; Lawson, 2004 ). Cognitive scientists sometimes argue that children are themselves “little scientists.” For example, children with little or no formal training in the process of science can propose functional hypotheses to explain a natural event ( Vosniadou & Brewer, 1992 ) and causal hypotheses to explain how one event in nature may affect another ( Samarapungavan & Wiers, 1997 ). Have we, the science educators, excised reasoning skill from our students?

For the Intel ISEF Indianapolis survey and other surveys I report next, I followed the definitions of hypotheses described above, as candidate explanations or generalizations for observations seen in nature. If a proposed explanation or generalization of a pattern is valid, then we can anticipate (predict) a particular outcome from an experiment or that we will see the pattern elsewhere in nature. Therefore, a scientific hypothesis can lead to predictions ( Singer, 2007 ; Campbell et al., 2008 ) but is not, itself, “just a prediction” (a very common misconception).

My interest in student misunderstanding of the hypothesis was piqued at the 2006 Intel ISEF, so colleagues and I have now surveyed 1864 student projects at eight Intel ISEF competitions (2006, 2008–2014; Table 1 ). Students in the sample identified hypotheses on 1448 (78%) of these projects but wrote predictions 81.2% of the time; they wrote candidate explanations or generalizations on only 272 (18.8%) of the projects ( Table 2 ). Failure to write hypotheses was consistently greater than success across years, and the two groups were statistically distinguishable (paired t-test: t = 20.55, df = 7, P < 0.001). Informal interviews with students revealed that while some could explain their research as hypothesis-driven, these students could not avoid predictive statements (e.g., “If I do X, then Y will happen”).

In addition to the surveys conducted at Intel ISEF, I analyzed 66 current middle school, high school, and college science textbooks by assessing all NOS chapters, all laboratory prompts, and glossaries. Fifty-four of the 66 science textbooks included instruction for understanding the hypothesis; 12 (18%) did not contain any mention of the hypothesis. Forty-two percent of textbooks that mentioned the hypothesis failed by confusing it with a prediction in either (1) the definition of the hypothesis, (2) an example hypothesis, or (3) a lab prompt (e.g., “Propose a hypothesis about what will happen…”) (for more examples, see Table 3 ). The largest proportion (13 of 17; 76%) of textbooks with this confused definition and/or use of the term hypothesis was in the middle school sample. Six (17%) of the 35 high school science textbooks failed in at least one of the assessed categories. The 14 textbooks designed for the college market (and used in our upper-level, IB, and AP classes) fared best; only one (7%), a biology textbook, failed to teach the hypothesis as distinct from the prediction.

I surveyed 17 preservice science teachers in a graduate-level teacher preparation course focused on NOS at the University of Colorado; and 59 biology teachers, selected at random (a convenience sample), at the 2011 annual meeting of the National Association of Biology Teachers (NABT). I gave both groups (on the first day of the term for the students in the science education course) a “pop quiz” on paper that asked them to (1) write a definition of the hypothesis in science; and, after reading a set of observations, (2) write a hypothesis about the observations that could be tested with an experiment. In the science education course, 5 of the 17 teacher-candidates (29%) showed mastery of the hypothesis, while 12 (71%) confused the hypothesis with the prediction. Less than half of all responders (27/59; 45%) at the NABT meeting exhibited a genuine understanding of scientific hypotheses. Thirteen (48%) of the 27 responders with correct understanding were biology teachers with Ph.D. degrees.

As a comparison, Lawson (2002) reported that in a sample of preservice middle and high school biology teachers, 96% “confused hypotheses with predictions and agreed with the statement that a hypothesis is an educated guess of what will be observed under certain conditions.” If this situation is not addressed explicitly, teachers are likely to pass this misunderstanding on to their students.

I analyzed 300 peer-reviewed, published scientific papers that are part of a teaching collection I have accumulated over several years of teaching various biology courses. The papers are mostly from fields of biology in which hypothesis testing is common, but other fields of science are also represented, as well as science education papers (including several papers published in The American Biology Teacher ). Sixty-two percent (186/300) of the scientific papers analyzed use some form of the term ( hypothesis , hypotheses , hypothesize , or hypothesized ), and 12.3% (23/186) mislabel predictions as hypotheses. Again, see Table 2 for examples of incorrectly and correctly written hypotheses from students, textbooks, teachers, scientists, and science educators.

Many textbooks oversimplify the definition of the hypothesis to an educated guess . But as McComas (1996) asks, “An educated guess about what?” Some textbooks do better; in their popular upper-level textbook, Biology , Campbell et al. (2008) define the hypothesis as “A tentative answer to a well-framed question – an explanation on trial” (p. 19) ( Table 3 ). However, getting to that tentative answer or explanation is not as easy as it seems, and many scholars have written about it.

Generalizing & Explanatory Hypotheses

McComas (1996 , 2004 , 2015 ) explains that observations of natural phenomena can produce two strands of hypothetical reasoning: generalizations and explanations. We often use generalizing hypotheses to summarize patterns we observe in nature, and we can refer to these types of hypotheses as immature laws . If the generalizations hold true over and over again, they become established laws of nature. We then use explanatory hypotheses to provide reasons for the generalizations. Explanatory hypotheses can also be referred to as immature theories , because if the explanations survive various angles of rigorous testing they become established theories. Thus, theories can explain laws but never become laws.

As an example, consider Harvard University evolutionary biologist Jonathan Losos, who, with his colleagues, studies the Anolis lizards of the Caribbean Islands. One specific pattern the researchers have consistently observed is that some anoles (e.g., Anolis valencienni ) living on narrow twigs in their forest habitats have short legs ( Losos & Schneider, 2009 ). This observed pattern produces the generalization (generalizing hypothesis or immature law ) that particular body shapes and sizes in anoles are linked to particular habitats, and we can predict that anoles discovered living on twigs in forests on other islands will also have short legs. Losos and his colleagues proposed that adaptation to their twig habitats by way of natural selection was a likely explanation (explanatory hypothesis or immature theory ) for the pattern of short-legged anoles living on twigs. In one experiment to test the twig adaptation hypothesis, small breeding populations of long-legged trunk anoles ( A. sangrei ) were placed on small anole-free islands with only small-twigged bushes as habitat ( Losos et al., 2001 ). The prediction that follows the twig adaptation hypothesis is that, after several generations, the surviving anole population would have shorter legs as the environment and natural selection sift out the individuals with longer legs that are unable to use the twiggy habitat efficiently. Indeed, later generations of the anoles had significantly shorter legs than their ancestors. Figure 1 illustrates how these ideas are applied to the Anolis lizard example. Teachers might use a figure like this one in direct instruction to explain the situation – or ask students to create one after reading a scientific paper, to check for understanding.

Two pathways to theories and laws by way of explanatory hypotheses and generalizing hypotheses. Note that both types can generate predictions and that explanatory hypotheses and their resulting theories can provide explanations for generalizing hypotheses and their resulting laws, respectively.

Abduction, Deduction, & Induction

In the above example, Losos and his colleagues moved through several levels of logic that have been summarized by Lawson (2010) . These levels form the basic inferences of scientific reasoning, argumentation, and discovery – they are abduction, deduction, and induction. In noticing the short legs on twig anoles and that they moved easily in their twig habitat, the researchers proposed that the short legs were an adaptation driven by the uniqueness of the twig habitat. Proposing that the twig habitat may have driven the twig anoles to evolve short legs required some imagination and ingenuity on the part of Losos and his colleagues – a logical strategy in science called abduction and also known as the “creative leap” ( Langley, 1999 ). However, sometimes the abductive strategy involves literally abducting (figuratively stealing) an idea from the results of an earlier study. Indeed, adaptation had already been shown as an explanation for traits in other species. For example, different beak shapes and sizes of the Galápagos finches (e.g., the medium ground finch, Geospiza fortis ) function as adaptations to different food resources. Perhaps Losos and his colleagues saw the connection between the short legs of the anoles and their twig habitats as an analogy to the small beaks of the medium and small ground finches and the soft seeds the birds eat. In short, abductive reasoning produces explanatory hypotheses , sometimes through leaps of creativity.

If adaptation by natural selection is a reasonable hypothesis for the short legs on the twig anoles, then a logical consequence is that long-legged anoles placed in habitats with only twigs as perches would evolve shorter legs. This second logical strategy is called deduction – the researchers deduced an outcome of an experiment, a prediction, given the “adaptation by natural selection” hypothesis. Thus, deductive reasoning tests ideas with predictions .

When Losos and his colleagues looked at the results of their experiment, they found that the long-legged anoles had evolved shorter legs. They thus logically concluded that the result was in support of their twig habitat hypothesis and was also in support of established natural selection theory. This final logical step is called induction : if the observed result matches the predicted outcome, then the hypothesis is supported.

The process described above is often referred to in textbooks as the hypothetico-deductive strategy of “the scientific method.” It is important to point out here that hypothetico-deductive reasoning, coupled with induction, is not without problems. First, a logical fallacy of induction is affirming the hypothesis without considering other explanations – there may be other hypotheses that explain the observed result. The case may simply be that females prefer to mate with short-legged males. Indeed, false hypotheses can produce true predictions. A second problem with induction is that in designing and carrying out our experiments and affirming our hypotheses, we may unknowingly be making several assumptions, also called auxiliary hypotheses , that if violated throw doubt on our conclusions. For example, Losos and his colleagues assume that leg length in anoles is a strongly heritable trait, similar to beak size in finches. If the trait is not heritable, they will not see their predicted result.

Solving the Problem of “Hypothesis” in the Science Classroom

The results of the various surveys reported here are evidence that many of our students are not learning how to formulate and propose hypotheses to drive their scientific studies. Even our best science students, those who qualify for the Intel ISEF, are generating predictions but calling them “hypotheses.” These mistakes likely arise from several correctable teaching approaches. First – and perhaps the most commonly observed error in teaching hypothesis writing – is having students write “if…, then…” statements, where the if phrase is actually an experimental method, and the then phrase is a specific prediction. For example, a textbook, a teacher, or a student may propose the prediction, “ If fertilizer is added to the soil, then the plants will grow taller ,” but call it a hypothesis. Textbooks, teachers, students, and scientists who propose predictions in place of explanations are skipping abduction and analogical reasoning and proceeding directly to making predictions ( Lawson, 2004 ).

The if–then mistake is correctable. For example, when my students verbalize or write predictions and call or label them “hypotheses,” I point out the mistake, but then ask them how or why they are able to make those predictions. Students invariably begin their answers with “Because…” and often end up stating something close to the hypothesis they are testing. Using this strategy, we can guide our students toward a generalizing hypothesis or help them work through analogical reasoning and abduct an explanatory hypothesis. An additional strategy to help students delineate the hypothesis from the prediction is to have students write predictions and label them as predictions when they are planning their investigations. Perhaps the most critical component of this pedagogical strategy is that students become focused on keeping their explanations (generalizing or explanatory hypotheses) as completely separate statements from their predictions.

A second, egregious, and all too common practice is when teachers require students to write hypotheses for “canned” lab activities, the likely objective of which is simply to make determinations, such as the value of a physical constant ( Yip, 2007 ). In these cases, teachers can help students write generalizing hypotheses that explain patterns, but only after students have made some observations and recorded some data. In all cases, teachers may consider providing students a flow chart, similar to Figure 1 , that helps them move through the two strands of generating explanatory and generalizing hypotheses and their related predictions.

Finally, teachers are advised to take a close look at the textbooks they are using and carefully assess how the textbooks define and use hypothesis . They may indeed be using a textbook that confuses students on some level about what hypotheses are.

Correcting this confusion – between the hypothesis and the prediction in particular, and about NOS in general – will not happen overnight, or even within the next few weeks, but it does begin with teachers like you .

Science is an essential course in a student's formal education, but many have demonstrated that misunderstanding of NOS by students and teachers can be a major challenge. Perhaps the most important goal of science education in a democracy is to produce a future consensus of public policy makers and an informed electorate who have a scientific understanding of the natural world. Indeed, a lack of understanding of NOS has made it far too easy today for science denial and pseudoscience to influence personal and public decision making ( Flammer, 2006 ). Science educators must teach students how to use the logical strategies of scientific reasoning and how to employ the procedures for obtaining meaningful and credible knowledge through scientific results that will contribute to scientific knowledge and to the formation of effective, evidence-based public policy ( Dias et al., 2004 ; Forrest, 2011 ). The public must understand how science works, and I am convinced that we can produce a more scientifically literate public if we commit to a greater focus in science education on the nature of science, and starting with the hypothesis.

The author thanks the Boulder Valley School District and Cordon-Pharma Colorado for financial support for several trips to the Intel International Science and Engineering Fairs. H. Ayi-Bonte, K. Donley, H. Petach, and A. Smith contributed to data collection at various Intel ISEF events. Two anonymous reviewers and H. Ayi-Bonte, H. Petach, J. S. Levine, H. Quinn, S. M. Zerwin, J. M. Strode, and W. F. McComas provided invaluable comments on the manuscript.

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Subject: Hypothesis Generation in BiologyA Science Teaching Challenge &amp; Potential Solution

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4.14: Experiments and Hypotheses

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Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

• No. This statement is not testable or falsifiable.
• No. This statement is not testable.
• No. This statement is not falsifiable.
• Yes. This statement is testable and falsifiable.

[reveal-answer q=”429550″] Show Answers [/reveal-answer] [hidden-answer a=”429550″]

• d: Yes. This statement is testable and falsifiable. This could be tested with a number of different kinds of observations and experiments, and it is possible to gather evidence that indicates that air pollution is not linked with asthma.
• a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

[/hidden-answer]

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

• Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
• There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
• Some types of HPV can cause cervical cancer.
• About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

• HPV causes cervical cancer.
• People should not have unprotected sex with many partners.
• People who get the vaccine will not get HPV.
• The HPV vaccine will protect people against cancer.

[reveal-answer q=”20917″] Show Answer [/reveal-answer] [hidden-answer a=”20917″]Hypothesis A is not the best choice because this information is already known from previous studies. Hypothesis B is not testable because scientific hypotheses are not value statements; they do not include judgments like “should,” “better than,” etc. Scientific evidence certainly might support this value judgment, but a hypothesis would take a different form: “Having unprotected sex with many partners increases a person’s risk for cervical cancer.” Before the researchers can test if the vaccine protects against cancer (hypothesis D), they want to test if it protects against the virus. This statement will make an excellent hypothesis for the next study. The researchers should first test hypothesis C—whether or not the new vaccine can prevent HPV.[/hidden-answer]

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—his or her views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

• This group is given a placebo.
• This group is deliberately infected with HPV.
• This group is given nothing.
• This group is given the HPV vaccine.

[reveal-answer q=”918962″] Show Answers [/reveal-answer] [hidden-answer a=”918962″]

• a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
• d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

• Sex (all of the subjects will be female)
• Presence or absence of the HPV vaccine
• Presence or absence of HPV (the virus)

[reveal-answer q=”68680″]Show Answer[/reveal-answer] [hidden-answer a=”68680″]Answer b. Presence or absence of the HPV vaccine. This is the variable that is different between the control and the experimental groups. All the subjects in this study are female, so this variable is the same in all groups. In a well-designed study, the two groups will be of similar age. The presence or absence of the virus is what the researchers will measure at the end of the experiment. Ideally the two groups will both be HPV-free at the start of the experiment.

List three control variables other than age.

[practice-area rows=”3″][/practice-area] [reveal-answer q=”903121″]Show Answer[/reveal-answer] [hidden-answer a=”903121″]Some possible control variables would be: general health of the women, sexual activity, lifestyle, diet, socioeconomic status, etc.

What is the dependent variable in this experiment?

• Sex (male or female)
• Rates of HPV infection
• Age (years)

[reveal-answer q=”907103″]Show Answer[/reveal-answer] [hidden-answer a=”907103″]Answer b. Rates of HPV infection. The researchers will measure how many individuals got infected with HPV after a given period of time.[/hidden-answer]

Contributors and Attributions

• Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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• v.10(Suppl 10); 2009

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

1 Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ The Netherlands

M Scott Marshall

Andrew p gibson.

2 Swammerdam Institute for Life Science, University of Amsterdam, Amsterdam, 1018 WB The Netherlands

Martijn Schuemie

3 BioSemantics group, Erasmus University of Rotterdam, Rotterdam, 3000 DR The Netherlands

Sophia Katrenko

Willem robert van hage.

4 Business Informatics, Faculty of Sciences, Vrije Universiteit, Amsterdam, 1081 HV The Netherlands

Konstantinos Krommydas

Pieter w adriaans.

Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes.

We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence.

We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation.

In order to study a biomolecular mechanism such as epigenetic gene control (Figure ​ (Figure1) 1 ) and formulate a new hypothesis, we usually integrate various types of information to distil a comprehensible model. We can use this model to discuss with our peers before we test the model in the laboratory or by comparison to available data. A typical hypothesis is based on one's own knowledge, interpretations of experimental data, the opinions of peers, and the prior knowledge that is contained in literature. Many Web resources are available for molecular biologists to access available knowledge, of which Entrez PubMed, hosted by the US National Center for Biotechnology Information (NCBI), is probably the most used by molecular biologists. The difficulty of information retrieval from literature reveals the scale of today's information overload: over 17 million biomedical documents are now available from PubMed. Also considering the knowledge that did not make it to publication or that is stored in various types of databases and file systems, many scientists find it increasingly challenging to ensure that all potentially relevant facts are considered whilst forming a hypothesis. Support for extracting and managing knowledge is therefore a general requirement. Developments in the area of the Semantic Web and related areas such as information retrieval are making it possible to create applications that will support the task of hypothesis generation. First, RDF and OWL provide us with a way to represent knowledge in a machine readable format that is amenable to machine inference [ 1 , 2 ]. Ontologies have become an important source of knowledge in molecular biology. Many ontologies have been created and many types of application have become possible [ 3 ], with the life sciences providing a key motivation of addressing the information management problem that arises from high throughput data collection [ 4 , 5 ]. A downside to the popularity of bio-ontologies is that their number and size have become overwhelming when attempting to discover the best representation for one's personal hypothesis. Moreover, building a biological ontology is usually associated with a community effort where consensus is sought for clear descriptions of biological phenomena [ 6 ]. The question arises how an experimental biologist/bioinformatician can apply Semantic Web languages when the primary aim is not to build a comprehensive ontology for a community, but to represent a personal hypothesis for a particular biomolecular mechanism. Therefore, we explored an approach to semantic modeling that emphasizes the creation of a personal model within the scope of one hypothesis, but without precluding integration with other ontologies. Secondly, information retrieval and information extraction techniques can be used to elucidate putative knowledge to consider for a hypothesis by selecting relevant data and recognizing biological entities (e.g. protein names) and relations in text [ 5 , 8 ]. For instance, tools and algorithms have been developed that match predefined sets of biological terms [ 7 , 8 ], or that use machine learning algorithms to recognize entities and extract relations based on their context in a document [ 9 ]. These techniques can also be used to extend an ontology [ 10 , 11 ]. Several tools exist for text mining (See, for instance [ 8 ]), but for a methodology to be attractive to practitioners of experimental molecular biology we would like a method that is more directly analogous to wet laboratory experimentation. Workflow management systems offer a platform for in silico experimentation [ 12 – 14 ] where, for example, data integration [ 5 , 15 ], and systematic large-scale analysis [ 16 ] have been implemented. Workflows can also be shared on the web such as accomplished in myExperiment [ 17 ]. In a workflow, the steps of a computational experiment are carried out by individual components for which Web Services provide a common communication protocol [ 18 ]. We adopted the workflow paradigm for the design and execution of a reusable knowledge extraction experiment. The main services in the workflow are from the 'Adaptive Information Disclosure Application' toolkit (AIDA) that we are developing for knowledge management applications [ 19 ] and this document). The output enriches a knowledge base with putative biological relations and corresponding evidence. The approach is not limited to text mining but can be applied to knowledge extracted during any computational experiment. The advantage of routinely storing extracted knowledge is that it enables us to perform posterior analysis across many experiments.

Cartoon model for the mechanism of chromatin condensation and decondensation . Models for condensation and decondensation of chromatin, a determinant of transcriptional activity, involves enzymes for histone acetylation (HAT) and histone deacetylase (HDAC), DNA methylation, and methylation of histone H3K9 [ 47 ]. Cartoon representations are a typical means for scientific discourse for molecular biologists.

We present the methodology in the following order: 1) a description of representing prior knowledge through proto-ontologies; 2) extension of the proto-ontologies by a workflow that adds instances to a semantic repository preloaded with the proto-ontologies; 3) a description of how to query the knowledge base; 4) a description of the toolkit that we use for knowledge extraction and knowledge management. Data and references are accessible from pack 58 on myExperiment.org [ 20 ].

Model representation in OWL

Different types of knowledge.

Step one of our methodology is to define machine readable 'proto-ontologies' to represent our biological hypothesis within the scope of an experiment. The experiment in this case is a procedure to extract protein relations from literature. Our approach is based on the assumption that knowledge models can grow with each experiment that we or others perform. Therefore, we created a minimal OWL ontology of the relevant biological domain entities and their biological relations for our knowledge extraction experiment. The purpose of the experiment is to populate (enrich) the proto-ontologies with instances derived from literature. We also modeled the evidence that led to these instances. For instance, the process by which a protein name was found and in which document it was found. We find a clash between our intention of enriching a biological model, and the factual observations of a text mining procedure such as 'term', 'interaction assertion', or 'term collocation'. For example, it is obvious that collocation of the terms 'HDAC1' and 'p53' in one abstract does not necessarily imply collocation of the referred proteins in a cell. In order to avoid conflation of knowledge from the different stages of our knowledge extraction process, we purposefully kept distinct OWL models. This lead to the creation of the following models that will be treated in detail below:

❑ Biological knowledge for our hypothesis (Protein, Association)

❑ Text (Terms, Document references)

❑ Knowledge extraction process (Steps of the procedure)

❑ Extraction procedure implementation (Web Service and Workflow runs)

❑ Mapping model to integrate the above through references.

❑ Results (Instances of extracted terms and relations)

Biological model

For the biological model, we started with a minimal set of classes designed for hypotheses about proteins and protein-protein associations (Figure ​ (Figure2). 2 ). This model contains classes such as 'Protein', 'Interaction' and 'Biological Model'. We regard instances in the biological model as interpretations of certain observations, in our case, of text mining results. We also do not consider instances of these classes as biological facts; they are restricted to a hypothetical model in line with common practice in experimental biology. The evidence for the interpretation is important, but it is not within the scope of this model. In the case of text mining, evidence is modeled by the text, text mining, and implementation models.

Graphical representation of the biological domain model in OWL and example instances . This proto-ontology contains classes for instances that may be relevant in hypotheses about chromatin (de)condensation. HDAC1 and PCAF are example instances representing proteins implied in models about this process and known to interact. In this and following figures, red diamonds represent instances, dashed arrows connected to diamonds represent instance-of relations and blue dashed arrows represent properties between classes or instances. Inverse relations are not shown for clarity. Protein Association represents the reified relation in which two (or more) proteins participate. Instances of 'BiologicalModel' represent an abstraction of a biological hypothesis that can be partially represented by user queries, proteins provided by the user, and proteins discovered by text mining.

A model of the structure of documents and statements therein is less ambiguous than the biological model, because we can directly inspect concrete instances such as (references to) documents or pieces of text (Figure ​ (Figure3). 3 ). We can be sure of the scope of the model and we can be clear about the distinction between classes and instances because we computationally process the documents. This model contains classes for documents, protein or gene names, and mentions of associations between proteins or genes.

Graphical representation of proto-ontology for entities in text and example instances . This proto-ontology contains classes of instances for documents, terms, and statements found in the text of the documents. The latter relation is represented by 'component of' properties. The instances represent concrete observations in text. Properties such as 'relates' and 'relatesBy' represent their interrelations. Example instances are shown for protein names 'HDAC1' and 'p68' and an assertion suggesting a relation between these two proteins.

Text mining model

Next, we created a model for the knowledge extraction process. This model serves to retrieve the evidence for the population of our biological model (Figure ​ (Figure4). 4 ). It contains classes for information retrieval and information extraction such as 'collocation process' and properties such as 'discovered by'. We also created classes to contain text mining specific information such as the likelihood of terms being found in the literature. This allows us to inspect the uncertainty of certain findings. Because any procedure could be implemented in various ways, we created a separate model for the implementation artifacts.

Graphical representation of the proto-ontology for the text mining process . This proto-ontology contains the classes for instances of the processes by which a knowledge extraction experiment is performed. The darker coloured classes represent restriction classes for instances that have at least one 'discoveredBy' property defined.

Workflow model

For more complete knowledge provenance, we also created a model representing the implementation of the text mining process as a workflow of (AIDA) Web Services. Example instances are (references to) the AIDA Web Services, and runs of these services. Following the properties of these instances we can retrace a particular run of the workflow.

Mapping model

At this point, we have created a clear framework for the description of our biological domain and the documents and the text mining results as instances in our text and text mining ontologies. The next step is to relate the instances in the various models to the biological domain model. Our strategy is to initially keep the domain model simple at the class and object property level, and to map sets of instances from our results to the domain model. For this, we created an additional mapping model that defines reference properties between the models (Figure ​ (Figure5). 5 ). This allows us to see that an interaction between the proteins labeled 'p68' and 'HDAC1' in our hypothetical model is referred to by a mention of an association between the terms 'p68' and 'HDAC1', with a likelihood score that indicates how remarkable it is to find this combination in literature.

Graphical representation of the proto-ontology containing the mapping properties between the biological, text, and text mining models . The 'reference' properties connect the concrete observations captured in the text model with the model representations in the biological model. For instance, the discovered protein name 'HDAC1' in the text mining model refers to the protein labelled 'HDAC1' that is a component of an instance representing a chromatin condensation hypothesis.

In summary, we have created proto-ontologies that separate the different views on biomolecular knowledge derived from literature by a text mining experiment. We can create instances in each view and their interrelations (Figure ​ (Figure6). 6 ). This allows us to trace the experimental evidence for knowledge contained in the biological model. In a case of text mining such as ours, evidence is modeled by the document, text mining, and workflow models. A different type of computational experiment would require other models and new mappings to represent evidence.

Knowledge extraction workflow . The knowledge extraction workflow has three parts. The left part executes the steps of a basic text mining procedure: (i) extract protein names from the user query and add synonyms using the BioSemantics synonym service, (ii) retrieve documents from MedLine with the AIDA document search service, (iii) extract proteins with the AIDA named entity recognition service, (iv) calculate a ranking score for each discovery. The middle workflow converts the results from the text mining workflow to RDF using the biological model and the text model as template. The workflow on the right-side creates execution-level instances for the workflow components and couples these to the instances created in the middle workflow. The blue rectangles represent inputs and outputs. The pink rectangles represent sub-workflows.

Knowledge extraction experiment

In parallel to the part of the workflow that performs the basic text mining procedure, we designed a set of 'semantic' sub-workflows to convert the text mining results to instances of the proto-ontologies and add these instances to the AIDA knowledge base, including their interrelations (steps s N in Figure ​ Figure6). 6 ). The first step of this procedure is to initialize this knowledge base after which the proto-ontologies are loaded into the knowledge base, and references to the knowledge base are available for the rest of the workflow. The next step is to add instances for the following entities to the knowledge base: 1) the initial biological model/hypothesis, 2) the original input query, 3) the protein names it contains, and 4) the expanded query. We assumed that the input query and the proteins mentioned therein partially represent the biological model; each run of the workflow creates a new instance of a biological model unless the input query is exactly the same as in a previous experiment. Figure ​ Figure7 7 illustrates the creation of an instance of a biological model and its addition to the knowledge base, including the details for creating the RDF triples in Java. All the semantic sub-workflows follow a similar pattern (data not shown). The following sub-worfklow adds instances for retrieved documents to the knowledge base; it only uses the PubMed identifier. The sub-workflow that adds discovered proteins is critical to our methodology. It creates protein term instances from protein names in the Text ontology to which it also adds the collocation relations with the original query a and a 'discovered_in' relation with the document it was discovered in. In addition, it creates protein instances in the BioModel ontology and a biological association relation to the proteins found in the input query. Between term and protein instances in the different ontologies it creates reference relations. As a result, our knowledge base is populated with the discoveries of the text mining procedure and their biological interpretations still linked with the knowledge they are interpretations of. The final sub-workflow adds the calculated likelihood scores as a property of the protein terms in the knowledge base. Finally, to be able to retrieve more complete evidence from the knowledge base, we extended our models and workflow to accommodate typical provenance data (not shown). We created an ontology with classes for Workflow runs and Web Service runs. Using the same semantic approach as above we were able to store instances of these runs, including the date and time of execution.

Example RDF conversion workflow . This workflow creates an OWL instance for a biological hypothesis in RDF 'N3' format, and adds the RDF triples to the AIDA knowledge base with the 'addRDF' operation of the AIDA repository Web Service. The actual conversion is performed in the Java Beanshell 'Instantiate_Semantic_Type' of which the code is shown at the bottom. The sub-workflow has the hypothesis instance as output for use by other sub-workflows in the main workflow.

Querying the knowledge base

The result of running the workflow is that our knowledge base is enriched with instances of biological concepts and relations between those instances that can also tell us why the instances were created. We can examine the results in search of unexpected findings or we can examine the evidence for certain findings, for instance by examining the documents in which some protein name was found. An interesting possibility is to explore relations between the results of computational experiments that added knowledge to the knowledge base. To prove this concept we ran the workflow twice, first with "HDAC1 AND chromatin" as input, and then with "(Nutrition OR food) AND (chromatin OR epigenetics) AND (protein OR proteins)" as input. We were then able to retrieve three proteins that are apparently shared between the two biological models (see Figure ​ Figure8 8 for the RDF query): NF-kappaB (UniProt ID {"type":"entrez-protein","attrs":{"text":"P19838","term_id":"21542418","term_text":"P19838"}} P19838 ), p21 (UniProt ID {"type":"entrez-protein","attrs":{"text":"P38936","term_id":"729143","term_text":"P38936"}} P38936 ), and Bax (UniProt ID {"type":"entrez-protein","attrs":{"text":"P97436","term_id":"3023954","term_text":"P97436"}} P97436 ). If we would like to investigate the evidence by which these proteins were discovered we designed a query that traces the chain of evidence (Figure ​ (Figure9). 9 ). It retrieves the process by which the name of the protein was found, the service by which the process was implemented and its creator, the document from MedLine in which the protein name was discovered, and the time when this discovery service was run. For example, NF-KappaB was found on the 18 th of November 2008 in a paper with PubMed identifier 17540846, by a run of the 'AIDA CRF Named Entity Recognition service' based on 'conditional random fields trained on protein names', created by Sophia Katrenko.

Pseudo RDF query for extracting proteins related to two hypotheses . RDF queries are pattern matching queries. This query returns proteins that were found by mining for relations with two different hypotheses represented by two different user queries. The result is a table of protein descriptions and the two user queries.

Graphical representation of a 'chain of evidence query' . This RDF query matches patterns in the RDF graph created by the knowledge extraction workflow. The result is a table of protein identifiers, protein names, the process by which the proteins were found, the service that implemented this process, the date and time it was run, its creator, and the document that the service used as input and of which the protein name was a component.

The AIDA Toolkit for knowledge extraction and knowledge management

The methodology that we propose enables a 'do-it-yourself' approach to extracting knowledge that can support hypothesis generation. To support this approach, we are developing an open source toolkit called Adaptive Information Disclosure Application (AIDA). AIDA is a generic set of components that can perform a variety of tasks related to knowledge extraction and knowledge management, such as perform specialized search on resource collections, learn new pattern recognition models, and store knowledge in a repository. W3C standards are used to make data accessible and manageable with Semantic Web technologies such as OWL, RDF(S), and SKOS. AIDA is also based on Lucene and Sesame. Most components are available as web services and are open source under an Apache license. AIDA is composed of three main modules: Search, Learning, and Storage.

Search – the information retrieval module

AIDA provides components which enable retrieval from a set of documents given a query, similar to popular search engines such as Google, Yahoo!, or PubMed. To make a set of documents (a corpus) searchable, an 'index' needs to be created first [ 25 ]. For this the AIDA's configurable Indexer can be used. The Indexer and Search components are built upon Apache Lucene, version 2.1.0 [ 26 ], and, hence, indexes or other systems based on Lucene can easily be integrated with AIDA. The Indexer component takes care of the preprocessing (the conversion, tokenization, and possibly normalization) of the text of each document as well as the subsequent index generation. Different fields can be made retrievable such as title, document name, authors, or the entire contents. The currently supported document encodings are Microsoft Word, Portable Document Format (PDF), MedLine, XML, and plain text. The so-called "DocumentHandlers" which handle the actual conversion of each source file are loaded at runtime, so a handler for any other proprietary document encoding can be created and used instantly. Because Lucene is used as a basis, a plethora of options and/or languages are available for stemming, tokenization, normalization, or stop word removal which may all be set on a per-field, per-document type, or per-index basis using the configuration. An index can currently be constructed using either the command-line, a SOAP webservice (with the limitation of 1 document per call), or using a Taverna plugin.

Learning – the machine learning module

AIDA includes several components which enable information extraction from text data in the Learning module. These components are referred to as learning tools. The large community working on the information extraction task has already produced numerous data sets and tools to work with. To be able to use existing solutions, we incorporated some of the models trained on the large corpora into the named entity recognition web service NERecognizerService. These models are provided by LingPipe[ 27 ] and range from the very general named entity recognition (detecting locations, person and organization names) to the specific models in the biomedical field created to recognize protein names and other bio-entities. We specified several options for input/output, which gives us an opportunity to work with either text data or the output of the search engine Lucene. We also offer the LearnModel web service whose aim is to produce a model from annotated text data. A model is based on the contextual information and uses learning methods provided by Weka [ 28 ] libraries. Once such a model is created, it can be used by the TestModel web service to annotate texts in the same domain. In this paper we use an AIDA service that applies a service for an algorithm that uses sequential models, such as conditional random fields (CRFs)/CRFs have an advantage over Hiddem Markov Models because of their ability to relax the independence assumption by defining a conditional probability distribution over label sequences given an observation sequence. We used CRFs to detect named entities in several domains like acids of various lengths in the food informatics field or protein names in the biomedical field [ 9 ].

Named entity recognition constitutes only one subtask in information extraction. Relation extraction can be viewed as the logical next step after the named entity recognition is carried out [ 29 ]. This task can be decomposed into the detection of named entities, followed by the verification of a given relation among them. For example, given extracted protein names, it should possible to infer whether there is any interaction between two proteins. This task is accomplished by the RelationLearner web service. It uses an annotated corpus of relations to induce a model, which consequently can be applied to the test data with already detected named entities. The RelationLearner focuses on extraction of binary relations given the sentential context. Its output is a list of the named entities pairs, where the given relation holds.

The other relevant area for information extraction is detection of the collocations (or n-grams in the broader sense). This functionality is provided by the CollocationService which, given a folder with text documents, outputs the n-grams of the desired frequency and length.

Storage – the metadata storage module

AIDA includes components for the storage and processing of ontologies, vocabularies, and other structured metadata in the Storage module. The main component, also for the work described in this paper, is RepositoryWS, a service wrapper for Sesame – an open source framework for storage, inferencing and querying of RDF data on which most of this module's implementation is based [ 30 , 31 ]. ThesaurusRepositoryWS is an extension of RepositoryWS that provides convenient access methods for SKOS thesauri. The Sesame RDF repository offers an HTTP interface and a Java API. In order to be able to integrate Sesame into workflows we created a SOAP service that gives access to the Sesame Java API. We accommodate for extensions to other RDF repositories, such as the HP Jena, Virtuoso, Allegrograph repositories or future versions of Sesame, by implementing the Factory design pattern.

Complementary services from BioSemantics applications

One of the advantages of a workflow approach is the ability to include services created elsewhere in the scientific community ('collaboration by Web Services'). For instance, in our BioAID workflows operations are used for query expansion and validation of protein names by UniProt identifiers. AIDA is therefore complemented by services derived from text mining applications such as Anni 2.0 from the BioSemantics group [ 32 ]. The 'BioSemantics' group is particularly strong in disambiguation of the names of biological entities such as genes/proteins, intelligent biological query expansion (manuscript in preparation), and provision of several well known identifiers for biological entities through carefully compiled sets of names and identifiers around a biological concept.

User interfaces for AIDA

In addition to RDF manipulation within workflows as described in this document, several examples of user interactions have been made available in AIDA clients such as HTML web forms, AJAX web applications, and a Firefox toolbar. The clients access RepositoryWS for querying RDF through the provided Java Servlets. The web services in Storage have recently been updated from the Sesame 1.2 Java API to the Sesame 2.0 Java API. Some of the new features that Sesame 2.0 provides, such as SPARQL support and named graphs, are now being added to our web service API's and incorporated into our applications.

Our methodology for supporting the generation of a hypothesis about a biomolecular mechanism is based on a combination of tools and expertise from the fields of Semantic Web, e-Science, information retrieval, and information extraction. This novel combination has a number of benefits. First, the use of RDF and OWL removes the technical obstacle for making models interoperable with other knowledge resources on the Semantic Web although semantic interoperability will often require an alignment process to take place for more far reaching compatibility. The modeling approach that we propose is complementary to the efforts of communities such as the Open Biomedical Ontology (OBO) community. This community's stated purpose is to create an 'accurate representation of biological reality' by developing comprehensive domain ontologies and reconciling existing ontologies according to a number of governing principles [ 4 ]. Our ambitions are more modest. We start with a minimal model to represent a hypothesis, i.e. a particular model of reality. We define our own classes and properties within the scope of a knowledge extraction experiment, but because of the modularity supported by OWL this does not exclude integration with other ontologies. In fact, integration with existing knowledge resources enables a complementary approach for finding facts potentially relevant to a hypothesis. Clearly, in order to scale up our methodology to represent knowledge beyond the experiments of a small group of researchers, alignment with standards would have to be considered. Upper ontologies can facilitate integration (for an example see [ 33 ]), and we can benefit from the OBO guidelines and the tools that have been developed to convert OBO ontologies to OWL [ 33 – 35 ]. Another interesting possibility is the integration with thesauri based on the SKOS framework [ 36 ]. Relations between SKOS concepts (terms) are defined by simple 'narrower' and 'broader' relations that turn out to be effective for human computer interfaces, and may be the best option for labeling the elements in our semantic models. Instead of providing a text string as a human readable label, we could associate an element with an entry in a SKOS thesaurus, which is a valuable knowledge resource in itself. The SKOS format is useful as an approach for 'light-weight' knowledge integration that avoids the problems of ontological over-commitment associated with more powerful logics like OWL DL [ 37 ].

A second benefit of our methodology comes from the implementation of the knowledge extraction procedure as a workflow. The procedure for populating an ontology is similar to the one previously described by Witte et al. [ 38 ], but our implementation allows the accumulation of knowledge by repeatedly running the same workflow or adaptations of it. This enables us to perform posterior analyses over the results from several experiments by querying the knowledge base, for instance in a new workflow that uses the AIDA semantic repository service. Moreover, the approach is not limited to text mining. If one considers text documents as a particular form of data, we can generalize the principle to any computational experiment in which the output can be related to a qualitative biological model. As such, this work extends previous work on integration of genome data via semantic annotation [ 39 ]. In this case the annotation is carried out by a workflow. Considering that there are thousands of Web Services and hundreds of workflows available for bioinformaticians [ 17 ], numerous extensions to our workflow can be explored. In addition, the combination with a semantic model allows us to collect evidence information as a type of knowledge provenance during workflow execution. In this way, we were able to address the issue of keeping a proper log of what has happened to our data during computational experimentation, analogous to the lab journal typically required in wet labs [ 40 ]. Ideally, the knowledge provenance captured in our approach would be more directly supported by existing workflow systems. However, this is not yet possible. There seems to be a knowledge gap between workflow investigators and the users from a particular application domain with regard to provenance. We propose that workflow systems take care of execution level provenance and provide an RDF interface on which users can build their own provenance model. In this context, it will be interesting to see if we will be able to replace our workflow model and link directly to the light weight provenance model that is being implemented for Taverna 2 [ 41 ]. A third benefit is that the application of Semantic Web, Web Services, and workflows stored on myExperiment.org, allow all resources relevant to an experiment to be shared on the web, making our results more reproducible. We would like to increase the 'liquidity' of knowledge so that knowledge extracted from computational experiments can eventually fit into frameworks for scientific discourse (hypotheses, research statements and questions, etc.) such as Semantic Web Applications in Neuromedicine (SWAN) [ 42 ]. If it is to be global, interoperability across modes of discourse would require large scale consensus on how to express knowledge provenance, not only about knowledge produced from computational experiments but also from manual or human assertions. Some groups are attempting to address various aspects of this problem, such as the Scientific Discourse task force [ 43 ] in the W3C Semantic Web Health Care and Life Sciences Interest Group [ 44 ], the Concept Web Alliance [ 45 ] and the Shared Names initiative [ 46 ].

In this paper we demonstrate a methodology for a 'do it yourself' approach for the extraction and management of knowledge in support of generating hypotheses about biomolecular mechanisms. Our approach describes how one can create a personal model for a specific hypothesis and how a personal 'computational experiment' can be designed and executed to extract knowledge from literature and populate a knowledge base. A significant advantage of the methodology is the possibility it creates to perform analyses across the results of several of these knowledge extraction experiments. Moreover, the principle of semantic disclosure of results from a computational experiment is not limited to text mining. In principle, it can be applied to any kind of experiment of which the (interpretations of) results can be converted to semantic models, almost as a 'side effect' of the experiment at hand. Experimental data is automatically semantically annotated which makes it manageable within the context of its purpose: biological study. We consider this an intuitive and flexible way of enabling the reuse of data. With the use of Web Services from the AIDA Toolkit and others, we also demonstrated the exploitation of the expertise of computational scientists with diverse backgrounds, i.e. where knowledge sharing takes place at the level of services and qualitative models. We consider the demonstration of e-Science and Semantic Web tools for a personalized approach in the context of scientific communities to be one of the main contributions of our methodology. In summary, the methodology provides a basis for automated support for hypothesis formation in the context of experimental science. Future extensions will be driven by biological studies on specific biomolecular mechanisms such as the role of histone modifications in transcription. We also plan to evaluate general strategies for extracting novel ideas from a growing repository of structured knowledge.

Acknowledgements

We thank the myGrid team and OMII-UK for their support in applying their e-Science tools, and Machiel Jansen for his contribution to the early development of AIDA. This work was carried out in the context of the Virtual Laboratory for e-Science program (VL-e) and the BioRange program. These programs are supported by BSIK grants from the Dutch Ministry of Education, Culture and Science (OC&W). Special thanks go to Bob Hertzberger who made the VL-e project a reality.

This article has been published as part of BMC Bioinformatics Volume 10 Supplement 10, 2009: Semantic Web Applications and Tools for Life Sciences, 2008. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/10?issue=S10 .

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

Marco Roos, M. Scott Marshall, and Pieter Adriaans conceived the BioAID concept and scenario. Marco Roos, Andrew Gibson and M. Scott Marshall conceived the semantic modeling approach. Marco Roos created the ontological models and implemented the workflow. M. Scott Marshall coordinated the development of AIDA. Martijn Schuemie, Edgar Meij, Sophia Katrenko, and Willem van Hage and Konstantinos Krommydas, developed the synonym/UniProt service, the document retrieval service, the protein extraction service, and the semantic repository service respectively. All authors contributed to the overall development of our methodology.

Contributor Information

Marco Roos, Email: ln.avu.ecneics@soor .

M Scott Marshall, Email: ln.avu.ecneics@llahsram .

Andrew P Gibson, Email: [email protected] .

Martijn Schuemie, Email: [email protected] .

Edgar Meij, Email: moc.dilav@ton .

Sophia Katrenko, Email: moc.dilav@ton .

Willem Robert van Hage, Email: moc.dilav@ton .

Konstantinos Krommydas, Email: moc.dilav@ton .

Pieter W Adriaans, Email: [email protected] .

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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Original Research: Creating a Hypothesis

• Initial Steps

Creating a Hypothesis

• Research Designs and Methods
• Submitting a Research Plan for Review
• Performing the Research
• Analyzing the Data
• Writing the Research Paper

After following the initial steps, the researcher should be able to create a hypothesis that can be tested. A hypothesis is a proposed statement that is intended to explain a theory for why something happens. To create a solid hypothesis, make sure it is not listed as a question, but as a prediction statement. To create a research hypothesis there has to be both a dependent and independent variable, and an expected outcome. Independent variables are what may be changed in the experiment to create an outcome. The dependent variable is what the experiment is intended to measure based on changes made to the independent variable. Defining the expected outcome creates the predictive component of the hypothesis that can be tested. Incorporating these elements into a simple predictive statement ensures that you can determine an outcome from the experiment. Ensure that any variables are taken into consideration, and that the results from the hypothesis are measurable.

Types of Hypotheses

There are many types of hypotheses, but the seven most common are the following:

• Simple Hypothesis - Questions the relationship between the dependent and independent variables.
• Complex Hypothesis - Questions the effect of multiple dependent and independent variables.
• Empirical Hypothesis - Often called a working hypothesis, this question is applied to a specific field when looking for empirical evidence.
• Null Hypothesis - This is used to contradict the expected effect of dependent and independent variables. 
• Alternative Hypothesis - Several hypotheses are given, but as the experiment proceeds, the alternative hypothesis is introduced to reflect the conditions of the experiment. 
• Logical Hypothesis - These hypotheses are able to be verified using logic.
• Statistical Hypothesis ​ - A hypothesis of this type is one that can be proven using statistical analysis.

For more information about how to create a hypothesis, have a look at the  Fundamentals of Research Methodology  by Engwa Godwill. 

Based on the hypothesis created, the researcher will need to determine the best research design for the experiment. 

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• Next: Research Designs and Methods >>
• Last Updated: Jul 26, 2023 10:10 AM
• URL: https://libguides.apsu.edu/OriginalResearch

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• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

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To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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 >).

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.

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Biology Hypothesis

Delve into the fascinating world of biology with our definitive guide on crafting impeccable hypothesis thesis statements . As the foundation of any impactful biological research, a well-formed hypothesis paves the way for groundbreaking discoveries and insights. Whether you’re examining cellular behavior or large-scale ecosystems, mastering the art of the thesis statement is crucial. Embark on this enlightening journey with us, as we provide stellar examples and invaluable writing advice tailored for budding biologists.

What is a good hypothesis in biology?

A good hypothesis in biology is a statement that offers a tentative explanation for a biological phenomenon, based on prior knowledge or observation. It should be:

• Testable: The hypothesis should be measurable and can be proven false through experiments or observations.
• Clear: It should be stated clearly and without ambiguity.
• Based on Knowledge: A solid hypothesis often stems from existing knowledge or literature in the field.
• Specific: It should clearly define the variables being tested and the expected outcomes.
• Falsifiable: It’s essential that a hypothesis can be disproven. This means there should be a possible result that could indicate the hypothesis is incorrect.

What is an example of a hypothesis statement in biology?

Example: “If a plant is given a higher concentration of carbon dioxide, then it will undergo photosynthesis at an increased rate compared to a plant given a standard concentration of carbon dioxide.”

In this example:

• The independent variable (what’s being changed) is the concentration of carbon dioxide.
• The dependent variable (what’s being measured) is the rate of photosynthesis. The statement proposes a cause-and-effect relationship that can be tested through experimentation.

100 Biology Thesis Statement Examples

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Biology, as the study of life and living organisms, is vast and diverse. Crafting a good thesis statement in this field requires a clear understanding of the topic at hand, capturing the essence of the research aim. From genetics to ecology, from cell biology to animal behavior, the following examples will give you a comprehensive idea about forming succinct biology thesis statements.

Genetics: Understanding the role of the BRCA1 gene in breast cancer susceptibility can lead to targeted treatments.

2. Evolution: The finch populations of the Galápagos Islands provide evidence of natural selection through beak variations in response to food availability.

3. Cell Biology: Mitochondrial dysfunction is a central factor in the onset of age-related neurodegenerative diseases.

4. Ecology: Deforestation in the Amazon directly impacts global carbon dioxide levels, influencing climate change.

5. Human Anatomy: Regular exercise enhances cardiovascular health by improving heart muscle function and reducing arterial plaque.

6. Marine Biology: Coral bleaching events in the Great Barrier Reef correlate strongly with rising sea temperatures.

7. Zoology: Migration patterns of Monarch butterflies are influenced by seasonal changes and available food sources.

8. Botany: The symbiotic relationship between mycorrhizal fungi and plant roots enhances nutrient absorption in poor soil conditions.

9. Microbiology: The overuse of antibiotics in healthcare has accelerated the evolution of antibiotic-resistant bacterial strains.

10. Physiology: High altitude adaptation in certain human populations has led to increased hemoglobin production.

11. Immunology: The role of T-cells in the human immune response is critical in developing effective vaccines against viral diseases.

12. Behavioral Biology: Birdsong variations in sparrows can be attributed to both genetic factors and environmental influences.

13. Developmental Biology: The presence of certain hormones during fetal development dictates the differentiation of sex organs in mammals.

14. Conservation Biology: The rapid decline of bee populations worldwide is directly linked to the use of certain pesticides in agriculture.

15. Molecular Biology: The CRISPR-Cas9 system has revolutionized gene editing techniques, offering potential cures for genetic diseases.

16. Virology: The mutation rate of the influenza virus necessitates annual updates in vaccine formulations.

17. Neurobiology: Neural plasticity in the adult brain can be enhanced through consistent learning and cognitive challenges.

18. Ethology: Elephant herds exhibit complex social structures and matriarchal leadership.

19. Biotechnology: Genetically modified crops can improve yield and resistance but also pose ecological challenges.

20. Environmental Biology: Industrial pollution in freshwater systems disrupts aquatic life and can lead to loss of biodiversity.

21. Neurodegenerative Diseases: Amyloid-beta protein accumulation in the brain is a key marker for Alzheimer’s disease progression.

22. Endocrinology: The disruption of thyroid hormone balance leads to metabolic disorders and weight fluctuations.

23. Bioinformatics: Machine learning algorithms can predict protein structures with high accuracy, advancing drug design.

24. Plant Physiology: The stomatal closure mechanism in plants helps prevent water loss and maintain turgor pressure.

25. Parasitology: The lifecycle of the malaria parasite involves complex interactions between humans and mosquitoes.

26. Molecular Genetics: Epigenetic modifications play a crucial role in gene expression regulation and cell differentiation.

27. Evolutionary Psychology: Human preference for symmetrical faces is a result of evolutionarily advantageous traits.

28. Ecosystem Dynamics: The reintroduction of apex predators in ecosystems restores ecological balance and biodiversity.

29. Epigenetics: Maternal dietary choices during pregnancy can influence the epigenetic profiles of offspring.

30. Biochemistry: Enzyme kinetics in metabolic pathways reveal insights into cellular energy production.

31. Bioluminescence: The role of bioluminescence in deep-sea organisms serves as camouflage and communication.

32. Genetics of Disease: Mutations in the CFTR gene cause cystic fibrosis, leading to severe respiratory and digestive issues.

33. Reproductive Biology: The influence of pheromones on mate selection is a critical aspect of reproductive success in many species.

34. Plant-Microbe Interactions: Rhizobium bacteria facilitate nitrogen fixation in leguminous plants, benefiting both organisms.

35. Comparative Anatomy: Homologous structures in different species provide evidence of shared evolutionary ancestry.

36. Stem Cell Research: Induced pluripotent stem cells hold immense potential for regenerative medicine and disease modeling.

37. Bioethics: Balancing the use of genetic modification in humans with ethical considerations is a complex challenge.

38. Molecular Evolution: The study of orthologous and paralogous genes offers insights into evolutionary relationships.

39. Bioenergetics: ATP synthesis through oxidative phosphorylation is a fundamental process driving cellular energy production.

40. Population Genetics: The Hardy-Weinberg equilibrium model helps predict allele frequencies in populations over time.

41. Animal Communication: The complex vocalizations of whales serve both social bonding and long-distance communication purposes.

42. Biogeography: The distribution of marsupials in Australia and their absence elsewhere highlights the impact of geographical isolation on evolution.

43. Aquatic Ecology: The phenomenon of eutrophication in lakes is driven by excessive nutrient runoff and results in harmful algal blooms.

44. Insect Behavior: The waggle dance of honeybees conveys precise information about the location of food sources to other members of the hive.

45. Microbial Ecology: The gut microbiome’s composition influences host health, metabolism, and immune system development.

46. Evolution of Sex: The Red Queen hypothesis explains the evolution of sexual reproduction as a defense against rapidly evolving parasites.

47. Immunotherapy: Manipulating the immune response to target cancer cells shows promise as an effective cancer treatment strategy.

48. Epigenetic Inheritance: Epigenetic modifications can be passed down through generations, impacting traits and disease susceptibility.

49. Comparative Genomics: Comparing the genomes of different species sheds light on genetic adaptations and evolutionary divergence.

50. Neurotransmission: The dopamine reward pathway in the brain is implicated in addiction and motivation-related behaviors.

51. Microbial Biotechnology: Genetically engineered bacteria can produce valuable compounds like insulin, revolutionizing pharmaceutical production.

52. Bioinformatics: DNA sequence analysis reveals evolutionary relationships between species and uncovers hidden genetic information.

53. Animal Migration: The navigational abilities of migratory birds are influenced by magnetic fields and celestial cues.

54. Human Evolution: The discovery of ancient hominin fossils provides insights into the evolutionary timeline of our species.

55. Cancer Genetics: Mutations in tumor suppressor genes contribute to the uncontrolled growth and division of cancer cells.

56. Aquatic Biomes: Coral reefs, rainforests of the sea, host incredible biodiversity and face threats from climate change and pollution.

57. Genomic Medicine: Personalized treatments based on an individual’s genetic makeup hold promise for more effective healthcare.

58. Molecular Pharmacology: Understanding receptor-ligand interactions aids in the development of targeted drugs for specific diseases.

59. Biodiversity Conservation: Preserving habitat diversity is crucial to maintaining ecosystems and preventing species extinction.

60. Evolutionary Developmental Biology: Comparing embryonic development across species reveals shared genetic pathways and evolutionary constraints.

61. Plant Reproductive Strategies: Understanding the trade-offs between asexual and sexual reproduction in plants sheds light on their evolutionary success.

62. Parasite-Host Interactions: The coevolution of parasites and their hosts drives adaptations and counter-adaptations over time.

63. Genomic Diversity: Exploring genetic variations within populations helps uncover disease susceptibilities and evolutionary history.

64. Ecological Succession: Studying the process of ecosystem recovery after disturbances provides insights into resilience and stability.

65. Conservation Genetics: Genetic diversity assessment aids in formulating effective conservation strategies for endangered species.

66. Neuroplasticity and Learning: Investigating how the brain adapts through synaptic changes improves our understanding of memory and learning.

67. Synthetic Biology: Designing and engineering biological systems offers innovative solutions for medical, environmental, and industrial challenges.

68. Ethnobotany: Documenting the traditional uses of plants by indigenous communities informs both conservation and pharmaceutical research.

69. Ecological Niche Theory: Exploring how species adapt to specific ecological niches enhances our grasp of biodiversity patterns.

70. Ecosystem Services: Quantifying the benefits provided by ecosystems, like pollination and carbon sequestration, supports conservation efforts.

71. Fungal Biology: Investigating mycorrhizal relationships between fungi and plants illuminates nutrient exchange mechanisms.

72. Molecular Clock Hypothesis: Genetic mutations accumulate over time, providing a method to estimate evolutionary divergence dates.

73. Developmental Disorders: Unraveling the genetic and environmental factors contributing to developmental disorders informs therapeutic approaches.

74. Epigenetics and Disease: Epigenetic modifications contribute to the development of diseases like cancer, diabetes, and neurodegenerative disorders.

75. Animal Cognition: Studying cognitive abilities in animals unveils their problem-solving skills, social dynamics, and sensory perceptions.

76. Microbiota-Brain Axis: The gut-brain connection suggests a bidirectional communication pathway influencing mental health and behavior.

77. Neurological Disorders: Neurodegenerative diseases like Parkinson’s and Alzheimer’s have genetic and environmental components that drive their progression.

78. Plant Defense Mechanisms: Investigating how plants ward off pests and pathogens informs sustainable agricultural practices.

79. Conservation Genomics: Genetic data aids in identifying distinct populations and prioritizing conservation efforts for at-risk species.

80. Reproductive Strategies: Comparing reproductive methods in different species provides insights into evolutionary trade-offs and reproductive success.

81. Epigenetics in Aging: Exploring epigenetic changes in the aging process offers insights into longevity and age-related diseases.

82. Antimicrobial Resistance: Understanding the genetic mechanisms behind bacterial resistance to antibiotics informs strategies to combat the global health threat.

83. Plant-Animal Interactions: Investigating mutualistic relationships between plants and pollinators showcases the delicate balance of ecosystems.

84. Adaptations to Extreme Environments: Studying extremophiles reveals the remarkable ways organisms thrive in extreme conditions like deep-sea hydrothermal vents.

85. Genetic Disorders: Genetic mutations underlie numerous disorders like cystic fibrosis, sickle cell anemia, and muscular dystrophy.

86. Conservation Behavior: Analyzing the behavioral ecology of endangered species informs habitat preservation and restoration efforts.

87. Neuroplasticity in Rehabilitation: Harnessing the brain’s ability to rewire itself offers promising avenues for post-injury or post-stroke rehabilitation.

88. Disease Vectors: Understanding how mosquitoes transmit diseases like malaria and Zika virus is critical for disease prevention strategies.

89. Biochemical Pathways: Mapping metabolic pathways in cells provides insights into disease development and potential therapeutic targets.

90. Invasive Species Impact: Examining the effects of invasive species on native ecosystems guides management strategies to mitigate their impact.

91. Molecular Immunology: Studying the intricate immune response mechanisms aids in the development of vaccines and immunotherapies.

92. Plant-Microbe Symbiosis: Investigating how plants form partnerships with beneficial microbes enhances crop productivity and sustainability.

93. Cancer Immunotherapy: Harnessing the immune system to target and eliminate cancer cells offers new avenues for cancer treatment.

94. Evolution of Flight: Analyzing the adaptations leading to the development of flight in birds and insects sheds light on evolutionary innovation.

95. Genomic Diversity in Human Populations: Exploring genetic variations among different human populations informs ancestry, migration, and susceptibility to diseases.

96. Hormonal Regulation: Understanding the role of hormones in growth, reproduction, and homeostasis provides insights into physiological processes.

97. Conservation Genetics in Plant Conservation: Genetic diversity assessment helps guide efforts to conserve rare and endangered plant species.

98. Neuronal Communication: Investigating neurotransmitter systems and synaptic transmission enhances our comprehension of brain function.

99. Microbial Biogeography: Mapping the distribution of microorganisms across ecosystems aids in understanding their ecological roles and interactions.

100. Gene Therapy: Developing methods to replace or repair defective genes offers potential treatments for genetic disorders.

Scientific Hypothesis Statement Examples

This section offers diverse examples of scientific hypothesis statements that cover a range of biological topics. Each example briefly describes the subject matter and the potential implications of the hypothesis.

• Genetic Mutations and Disease: Certain genetic mutations lead to increased susceptibility to autoimmune disorders, providing insights into potential treatment strategies.
• Microplastics in Aquatic Ecosystems: Elevated microplastic levels disrupt aquatic food chains, affecting biodiversity and human health through bioaccumulation.
• Bacterial Quorum Sensing: Inhibition of quorum sensing in pathogenic bacteria demonstrates a potential avenue for novel antimicrobial therapies.
• Climate Change and Phenology: Rising temperatures alter flowering times in plants, impacting pollinator interactions and ecosystem dynamics.
• Neuroplasticity and Learning: The brain’s adaptability facilitates learning through synaptic modifications, elucidating educational strategies for improved cognition.
• CRISPR-Cas9 in Agriculture: CRISPR-engineered crops with enhanced pest resistance showcase a sustainable approach to improving agricultural productivity.
• Invasive Species Impact on Predators: The introduction of invasive prey disrupts predator-prey relationships, triggering cascading effects in terrestrial ecosystems.
• Microbial Contributions to Soil Health: Beneficial soil microbes enhance nutrient availability and plant growth, promoting sustainable agriculture practices.
• Marine Protected Areas: Examining the effectiveness of marine protected areas reveals their role in preserving biodiversity and restoring marine ecosystems.
• Epigenetic Regulation of Cancer: Epigenetic modifications play a pivotal role in cancer development, highlighting potential therapeutic targets for precision medicine.

Testable Hypothesis Statement Examples in Biology

Testability hypothesis is a critical aspect of a hypothesis. These examples are formulated in a way that allows them to be tested through experiments or observations. They focus on cause-and-effect relationships that can be verified or refuted.

• Impact of Light Intensity on Plant Growth: Increasing light intensity accelerates photosynthesis rates and enhances overall plant growth.
• Effect of Temperature on Enzyme Activity: Higher temperatures accelerate enzyme activity up to an optimal point, beyond which denaturation occurs.
• Microbial Diversity in Soil pH Gradients: Soil pH influences microbial composition, with acidic soils favoring certain bacterial taxa over others.
• Predation Impact on Prey Behavior: The presence of predators induces changes in prey behavior, resulting in altered foraging strategies and vigilance levels.
• Chemical Communication in Marine Organisms: Investigating chemical cues reveals the role of allelopathy in competition among marine organisms.
• Social Hierarchy in Animal Groups: Observing animal groups establishes a correlation between social rank and access to resources within the group.
• Effect of Habitat Fragmentation on Pollinator Diversity: Fragmented habitats reduce pollinator species richness, affecting plant reproductive success.
• Dietary Effects on Gut Microbiota Composition: Dietary shifts influence gut microbiota diversity and metabolic functions, impacting host health.
• Hybridization Impact on Plant Fitness: Hybrid plants exhibit varied fitness levels depending on the combination of parent species.
• Human Impact on Coral Bleaching: Analyzing coral reefs under different anthropogenic stresses identifies the main factors driving coral bleaching events.

Scientific Investigation Hypothesis Statement Examples in Biology

This section emphasizes hypotheses that are part of broader scientific investigations. They involve studying complex interactions or phenomena and often contribute to our understanding of larger biological systems.

• Genomic Variation in Human Disease Susceptibility: Genetic analysis identifies variations associated with increased risk of common diseases, aiding personalized medicine.
• Behavioral Responses to Temperature Shifts in Insects: Investigating insect responses to temperature fluctuations reveals adaptation strategies to climate change.
• Endocrine Disruptors and Amphibian Development: Experimental exposure to endocrine disruptors elucidates their role in amphibian developmental abnormalities.
• Microbial Succession in Decomposition: Tracking microbial communities during decomposition uncovers the succession patterns of different decomposer species.
• Gene Expression Patterns in Stress Response: Studying gene expression profiles unveils the molecular mechanisms underlying stress responses in plants.
• Effect of Urbanization on Bird Song Patterns: Urban noise pollution influences bird song frequency and complexity, impacting communication and mate attraction.
• Nutrient Availability and Algal Blooms: Investigating nutrient loading in aquatic systems sheds light on factors triggering harmful algal blooms.
• Host-Parasite Coevolution: Analyzing genetic changes in hosts and parasites over time uncovers coevolutionary arms races and adaptation.
• Ecosystem Productivity and Biodiversity: Linking ecosystem productivity to biodiversity patterns reveals the role of species interactions in ecosystem stability.
• Habitat Preference of Invasive Species: Studying the habitat selection of invasive species identifies factors promoting their establishment and spread.

Hypothesis Statement Examples in Biology Research

These examples are tailored for research hypothesis studies. They highlight hypotheses that drive focused research questions, often leading to specific experimental designs and data collection methods.

• Microbial Community Structure in Human Gut: Investigating microbial diversity and composition unveils the role of gut microbiota in human health.
• Plant-Pollinator Mutualisms: Hypothesizing reciprocal benefits in plant-pollinator interactions highlights the role of coevolution in shaping ecosystems.
• Chemical Defense Mechanisms in Insects: Predicting the correlation between insect feeding behavior and chemical defenses explores natural selection pressures.
• Evolutionary Significance of Mimicry: Examining mimicry in organisms demonstrates its adaptive value in predator-prey relationships and survival.
• Neurological Basis of Mate Choice: Proposing neural mechanisms underlying mate choice behaviors uncovers the role of sensory cues in reproductive success.
• Mycorrhizal Symbiosis Impact on Plant Growth: Investigating mycorrhizal colonization effects on plant biomass addresses nutrient exchange dynamics.
• Social Learning in Primates: Formulating a hypothesis on primate social learning explores the transmission of knowledge and cultural behaviors.
• Effect of Pollution on Fish Behavior: Anticipating altered behaviors due to pollution exposure highlights ecological consequences on aquatic ecosystems.
• Coevolution of Flowers and Pollinators: Hypothesizing mutual adaptations between flowers and pollinators reveals intricate ecological relationships.
• Genetic Basis of Disease Resistance in Plants: Identifying genetic markers associated with disease resistance enhances crop breeding programs.

Prediction Hypothesis Statement Examples in Biology

Predictive simple hypothesis involve making educated guesses about how variables might interact or behave under specific conditions. These examples showcase hypotheses that anticipate outcomes based on existing knowledge.

• Pesticide Impact on Insect Abundance: Predicting decreased insect populations due to pesticide application underscores ecological ramifications.
• Climate Change and Migratory Bird Patterns: Anticipating shifts in migratory routes of birds due to climate change informs conservation strategies.
• Ocean Acidification Effect on Coral Calcification: Predicting reduced coral calcification rates due to ocean acidification unveils threats to coral reefs.
• Disease Spread in Crowded Bird Roosts: Predicting accelerated disease transmission in densely populated bird roosts highlights disease ecology dynamics.
• Eutrophication Impact on Freshwater Biodiversity: Anticipating decreased freshwater biodiversity due to eutrophication emphasizes conservation efforts.
• Herbivore Impact on Plant Species Diversity: Predicting reduced plant diversity in areas with high herbivore pressure elucidates ecosystem dynamics.
• Predator-Prey Population Cycles: Predicting cyclical fluctuations in predator and prey populations showcases the role of trophic interactions.
• Climate Change and Plant Phenology: Anticipating earlier flowering times due to climate change demonstrates the influence of temperature on plant life cycles.
• Antibiotic Resistance in Bacterial Communities: Predicting increased antibiotic resistance due to overuse forewarns the need for responsible antibiotic use.
• Human Impact on Avian Nesting Success: Predicting decreased avian nesting success due to habitat fragmentation highlights conservation priorities.

How to Write a Biology Hypothesis – Step by Step Guide

A hypothesis in biology is a critical component of scientific research that proposes an explanation for a specific biological phenomenon. Writing a well-formulated hypothesis sets the foundation for conducting experiments, making observations, and drawing meaningful conclusions. Follow this step-by-step guide to create a strong biology hypothesis:

1. Identify the Phenomenon: Clearly define the biological phenomenon you intend to study. This could be a question, a pattern, an observation, or a problem in the field of biology.

2. Conduct Background Research: Before formulating a hypothesis, gather relevant information from scientific literature. Understand the existing knowledge about the topic to ensure your hypothesis builds upon previous research.

3. State the Independent and Dependent Variables: Identify the variables involved in the phenomenon. The independent variable is what you manipulate or change, while the dependent variable is what you measure as a result of the changes.

4. Formulate a Testable Question: Based on your background research, create a specific and testable question that addresses the relationship between the variables. This question will guide the formulation of your hypothesis.

5. Craft the Hypothesis: A hypothesis should be a clear and concise statement that predicts the outcome of your experiment or observation. It should propose a cause-and-effect relationship between the independent and dependent variables.

6. Use the “If-Then” Structure: Formulate your hypothesis using the “if-then” structure. The “if” part states the independent variable and the condition you’re manipulating, while the “then” part predicts the outcome for the dependent variable.

7. Make it Falsifiable: A good hypothesis should be testable and capable of being proven false. There should be a way to gather data that either supports or contradicts the hypothesis.

8. Be Specific and Precise: Avoid vague language and ensure that your hypothesis is specific and precise. Clearly define the variables and the expected relationship between them.

9. Revise and Refine: Once you’ve formulated your hypothesis, review it to ensure it accurately reflects your research question and variables. Revise as needed to make it more concise and focused.

10. Seek Feedback: Share your hypothesis with peers, mentors, or colleagues to get feedback. Constructive input can help you refine your hypothesis further.

Tips for Writing a Biology Hypothesis Statement

Writing a biology alternative hypothesis statement requires precision and clarity to ensure that your research is well-structured and testable. Here are some valuable tips to help you create effective and scientifically sound hypothesis statements:

1. Be Clear and Concise: Your hypothesis statement should convey your idea succinctly. Avoid unnecessary jargon or complex language that might confuse your audience.

2. Address Cause and Effect: A hypothesis suggests a cause-and-effect relationship between variables. Clearly state how changes in the independent variable are expected to affect the dependent variable.

3. Use Specific Language: Define your variables precisely. Use specific terms to describe the independent and dependent variables, as well as any conditions or measurements.

4. Follow the “If-Then” Structure: Use the classic “if-then” structure to frame your hypothesis. State the independent variable (if) and the expected outcome (then). This format clarifies the relationship you’re investigating.

5. Make it Testable: Your hypothesis must be capable of being tested through experimentation or observation. Ensure that there is a measurable and observable way to determine if it’s true or false.

6. Avoid Ambiguity: Eliminate vague terms that can be interpreted in multiple ways. Be precise in your language to avoid confusion.

7. Base it on Existing Knowledge: Ground your hypothesis in prior research or existing scientific theories. It should build upon established knowledge and contribute new insights.

8. Predict a Direction: Your hypothesis should predict a specific outcome. Whether you anticipate an increase, decrease, or a difference, your hypothesis should make a clear prediction.

9. Be Focused: Keep your hypothesis statement focused on one specific idea or relationship. Avoid trying to address too many variables or concepts in a single statement.

10. Consider Alternative Explanations: Acknowledge alternative explanations for your observations or outcomes. This demonstrates critical thinking and a thorough understanding of your field.

11. Avoid Value Judgments: Refrain from including value judgments or opinions in your hypothesis. Stick to objective and measurable factors.

12. Be Realistic: Ensure that your hypothesis is plausible and feasible. It should align with what is known about the topic and be achievable within the scope of your research.

13. Refine and Revise: Draft multiple versions of your hypothesis statement and refine them. Discuss and seek feedback from mentors, peers, or advisors to enhance its clarity and precision.

14. Align with Research Goals: Your hypothesis should align with the overall goals of your research project. Make sure it addresses the specific question or problem you’re investigating.

15. Be Open to Revision: As you conduct research and gather data, be open to revising your hypothesis if the evidence suggests a different outcome than initially predicted.

Remember, a well-crafted biology science hypothesis statement serves as the foundation of your research and guides your experimental design and data analysis. It’s essential to invest time and effort in formulating a clear, focused, and testable hypothesis that contributes to the advancement of scientific knowledge.

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Research Hypothesis Generator Online

• ️👍 Hypothesis Maker: the Benefits
• ️🔎 How to Use the Tool?
• ️🕵️ What Is a Research Hypothesis?
• ️⚗️ Scientific Method
• ️🔗 References

👍 Hypothesis Maker: the Benefits

Here are the key benefits of this null and alternative hypothesis generator.

🔎 Hypothesis Generator: How to Use It?

Whenever you conduct research, whether a 5-paragraph essay or a more complex assignment, you need to create a hypothesis for this study.

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To get a ready-made hypothesis idea, you need to:

• State the object of your study
• Specify what the object does
• Lay out the outcome of that activity
• Indicate the comparison group

Once all data is inserted into the fields, you can press the “Generate now” button and get the result from our hypothesis generator for research paper or any other academic task.

🕵️ What Is a Research Hypothesis?

A hypothesis is your assumption based on existing academic knowledge and observations of the surrounding natural world.

It also involves a healthy portion of intuition because you should arrive at an interesting, commonsense question about the phenomena or processes you observe.

The traditional formula for hypothesis generation is an “if…then” statement, reflecting its falsifiability and testability.

What do these terms mean?

• Testability means you can formulate a scientific guess and test it with data and analysis.
• Falsifiability is a related feature, allowing you to refute the hypothesis with data and show that your guess has no tangible support in real-world data.

For example, you might want to hypothesize the following:

If children are given enough free play time, their intelligence scores rise quicker.

You can test this assumption by observing and measuring two groups – children involved in much free play and those who don’t get free play time. Once the study period ends, you can measure the intelligence scores in both groups to see the difference, thus proving or disproving your hypothesis, which will be testing your hypothesis. If you find tangible differences between the two groups, your hypothesis will be proven, and if there is no difference, the hypothesis will prove false.

Null and Alternative Hypothesis

As a rule, hypotheses are presented in pairs in academic studies, as your scientific guess may be refuted or proved. Thus, you should formulate two hypotheses – a null and alternative variant of the same guess – to see which one is proved with your experiment.

The alternative hypothesis is formulated in an affirmative form, assuming a specific relationship between variables. In other words, you hypothesize that the predetermined outcome will be observed if one condition is met.

Watching films before sleep reduces the quality of sleep.

The null hypothesis is formulated in a negative form, suggesting that there is no association between the variables of your interest. For example:

Watching films before sleep doesn’t affect the quality of sleep.

⚗️ Creating a Hypothesis: the Key Steps

The development and testing of multiple hypotheses are the basis of the scientific method .

Without such inquiries, academic knowledge would never progress, and humanity would remain with a limited understanding of the natural world.

How can you contribute to the existing academic base with well-developed and rigorously planned scientific studies ? Here is an introduction to the empirical method of scientific inquiry.

Step #1: Observe the World Around You

Look around you to see what’s taking place in your academic area. If you’re a biology researcher, look into the untapped biological processes or intriguing facts that nobody has managed to explain before you.

What’s surprising or unusual in your observations? How can you approach this area of interest?

That’s the starting point of an academic journey to new knowledge.

Step #2: Ask Questions

Now that you've found a subject of interest, it's time to generate scientific research questions .

A question can be called scientific if it is well-defined, focuses on measurable dimensions, and is largely testable.

Some hints for a scientific question are:

• What effect does X produce on Y?
• What happens if the intensity of X’s impact reduces or rises?
• What is the primary cause of X?
• How is X related to Y in this group of people?
• How effective is X in the field of C?

As you can see, X is the independent variable , and Y is the dependent variable.

This principle of hypothesis formulation is vital for cases when you want to illustrate or measure the strength of one variable's effect on the other.

Step #3: Generate a Research Hypothesis

After asking the scientific question, you can hypothesize what your answer to it can be.

You don't have any data yet to answer the question confidently, but you can assume what effect you will observe during an empirical investigation.

For example, suppose your background research shows that protein consumption boosts muscle growth.

In that case, you can hypothesize that a sample group consuming much protein after physical training will exhibit better muscle growth dynamics compared to those who don’t eat protein. This way, you’re making a scientific guess based on your prior knowledge of the subject and your intuition.

Step #4: Hold an Experiment

With a hypothesis at hand, you can proceed to the empirical study for its testing. As a rule, you should have a clearly formulated methodology for proving or disproving your hypothesis before you create it. Otherwise, how can you know that it is testable? An effective hypothesis usually contains all data about the research context and the population of interest.

For example:

Marijuana consumption among U. S. college students reduces their motivation and academic achievement.

• The study sample here is college students.
• The dependent variable is motivation and academic achievement, which you can measure with any validated scale (e.g., Intrinsic Motivation Inventory).
• The inclusion criterion for the study's experimental group is marijuana use.
• The control group might be a group of marijuana non-users from the same population.
• A viable research methodology is to ask both groups to fill out the survey and compare the results.

Step #5: Analyze Your Findings

Once the study is over and you have the collected dataset, it's time to analyze the findings.

The methodology should also delineate the criteria for proving or disproving the hypothesis.

Using the previous section's example, your hypothesis is proven if the experimental group reveals lower motivational scores and has a lower GPA . If both groups' motivation and GPA scores aren't statistically different, your hypothesis is false.

Step #6: Formulate Your Conclusion

Using your study's hypothesis and outcomes, you can now generate a conclusion . If the alternative hypothesis is proven, you can conclude that marijuana use hinders students' achievement and motivation. If the null hypothesis is validated, you should report no identified relationship between low academic achievement and weed use.

Thank you for reading this article! Note that if you need to conduct a business analysis, you can try our free tools: SWOT , VRIO , SOAR , PESTEL , and Porter’s Five Forces .

❓ Research Hypothesis Generator FAQ

❓ what is a research hypothesis.

A hypothesis is a guess or assumption you make by looking at the available data from the natural world. You assume a specific relationship between variables or phenomena and formulate that supposition for further testing with experimentation and analysis.

❓ How to write a hypothesis?

To compose an effective hypothesis, you need to look at your research question and formulate a couple of ways to answer it. The available scientific data can guide you to assume your study's outcome. Thus, the hypothesis is a guess of how your research question will be answered by the end of your research.

❓ What is the difference between prediction and hypothesis?

A prediction is your forecast about the outcome of some activities or experimentation. It is a guess of what will happen if you perform some actions with a specific object or person. A hypothesis is a more in-depth inquiry into the way things are related. It is more about explaining specific mechanisms and relationships.

❓ What makes a good hypothesis?

A strong hypothesis should indicate the dependent and independent variables, specifying the relationship you assume between them. You can also strengthen your hypothesis by indicating a specific population group, an intervention period, and the context in which you'll hold the study.

🔗 References

• What is and How to Write a Good Hypothesis in Research?
• Research questions, hypotheses and objectives - PMC - NCBI
• Developing the research hypothesis - PubMed
• Alternative Hypothesis - SAGE Research Methods
• Alternative Hypothesis Guide: Definition, Types and Examples