research n meaning

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May 6, 2020 • Brooke Siem

How to Read a Scientific Journal Part 2: What is “n”?

Part of the reason why I’m able to learn what I’m learning is that my partner, Justin, is an academic. He’s built his career on reading, writing, and analyzing journal articles, which means he’s my first stop on the understanding research train. This is both great and terrible for me. On the one hand, I have an expert at my disposal. On the other hand, I have an expert at my disposal. What I think are straightforward questions turn into twenty-minute tirades that leave me more confused than before. No answer is ever simple, and I’ve been forced to accept that “it depends” is a valid conclusion.

“The more you research you read the more you’ll understand that every single study is fundamentally flawed,” he said to me yesterday. “Be careful about assumptions, because research studies are full of caveats and exceptions. They’re looking at one little sliver of one thing, and there’s no easy way to accurately translate that into something digestible and catchy for the media.”

All this because I asked him what  n meant in a paper.

What is “n”?

I assumed the n operated like it does algebra, standing for a constant throughout the entire paper. As it turns out, that is entirely incorrect. There are big N s and little  n s. The big N typically stands for population size while the little  n  stands for some sort of value. For example, if there are 1000 people in a school but only 200 of them were chosen for a study, N =1000 and n =200.

However, the n does not necessarily refer to human subjects and the meaning of that n can change with context. Using the paper from yesterday’s post as my example, we can see that there are a variety of values for n throughout different parts of the article. The first shows up in the abstract,  n =16 :

Reading the sentence before it, “antidepressants were significantly better than placebo in trials that had a low risk of bias,” this little  n refers to the number of studies analyzed that had a low risk of bias (16 studies.) Why they can’t just say, “In the 16 trials that had a low risk of bias…” I don’t know.

Further down the paper,  n  shows up again:

To understand what these  n s represent, we need to read for context. The previous page states, “The literature searches from databases and additional resources identified 2890 relevant titles.” In this case, n has to do with the number of studies analyzed, and the chart breaks down how the researchers began with 2890 studies (2864 records identified through database searching + 26 records identified through other sources) and whittled their relevant studies down to the 28 included in the meta-analysis.

To sum up: An  n is not an interpretation of the data but instead communicates some sort of numerical value. That value changes depending on what it’s referring to, so it’s always necessary to read for context.

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What is n in statistics.

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In the realm of statistics, the letter ‘n’ holds significant meaning. It represents the sample size, which plays a crucial role in various statistical analyses . A thorough understanding of the concept of ‘n’ is essential for researchers and analysts alike. Even when I went to do my statistics homework , I noticed how crucial understanding the “n” in statistics is.  In this article, we will delve into the significance of ‘n’ and explore its impact on statistical testing, reliability, and the overall validity of research findings.

What is ‘n’ in Statistics?

The term ‘n’ refers to the number of observations or individuals in a given sample. It signifies the size of the sample being analyzed and influences the accuracy and generalizability of the results obtained. Whether it’s a survey, experiment, or observational study, the value of ‘n’ is a key factor that statisticians consider.

The Importance of Sample Size

The significance of sample size stems from its direct influence on the outcome of statistical analyses. Let’s explore why sample size matters in statistical research.

One of the primary reasons for emphasizing a larger sample size is its ability to increase the chances of detecting a significant difference. By including more observations, researchers can better capture the variability present in the population under study. Consequently, a larger sample size enhances the statistical power of the analysis, enabling researchers to uncover meaningful patterns and effects.

While larger samples offer increased statistical power, it is important to acknowledge the associated costs. Collecting data from a larger sample often requires more resources, such as time, money, and manpower. Researchers must strike a balance between the desired sample size and the available resources to ensure an efficient study design without compromising accuracy.

The Role of ‘n’ in Statistical Significance

Statistical significance is a vital concept in research, indicating the likelihood of an observed difference being due to more than just chance. Understanding how ‘n’ influences statistical significance is crucial for drawing accurate conclusions.

To comprehend the impact of sample size on statistical significance, let’s consider an example.

Suppose we are conducting a study to evaluate the effectiveness of a particular diet regime on weight loss. We randomly select two groups of participants, one with a sample size of 20 and the other with 40, both drawn from the same population.

To visualize the relationship between sample size and statistical significance, we can examine the distribution curves for each scenario. The curves represent the possible sample means for the two groups, assuming no difference between the diet and no effect (null hypothesis).

In the case of the sample size of 20 (n=20), the curve is wider, indicating a broader range of potential weight changes. However, with a sample size of 40 (n=40), the curve narrows, suggesting a more precise reflection of the population mean. Consequently, a weight change of 3kg would be more statistically significant in the group with a sample size of 40, as it falls towards the extreme end of the distribution curve.

Reliability of Sample Mean

The reliability of the sample mean as a representation of the population mean is an essential aspect of statistical analysis. A larger sample size leads to a more accurate estimation of the population mean. This reliability is quantified using a measure called the standard error of the mean (se).

Understanding Standard Error

The standard error of the mean combines the standard deviation of the parent population (a fixed value) with the sample size (a variable we can control). A smaller standard error indicates a more precise estimate of the population mean.

Enhancing Accuracy with Larger Samples

Imagine conducting an exit poll during an election. Asking only two people about their voting preference would yield less reliable results compared to interviewing 2,000 individuals. Similarly, in statistical analysis, a larger sample size enhances the accuracy of the findings.

With a larger sample, the distribution of sample means becomes narrower and spikier, indicating a closer representation of the population mean. This precision enables researchers to detect even subtle differences or effects reliably.

Calculating the Optimum Sample Size

Determining the optimal sample size for a study involves careful consideration of various factors, including the specific difference researchers wish to detect and the standard deviation of the population.

By utilizing a formula that incorporates these parameters, researchers can estimate the sample size necessary to achieve statistically significant results. This calculation ensures that the study is adequately powered to detect meaningful effects without oversampling or underestimating the required sample size.

In the field of statistics, the sample size denoted by ‘n’ holds significant importance. A larger sample size increases the likelihood of detecting meaningful differences, although it comes with associated costs. By carefully considering the optimal sample size, researchers can enhance the reliability and validity of their findings. Understanding the role of ‘n’ in statistical significance and the reflection of the population mean enables researchers to design robust studies and draw accurate conclusions.

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Does a larger sample size always yield more accurate results?

Yes, a larger sample size generally leads to more accurate results. With a larger sample, the variability within the population is better represented, increasing the precision of estimates. However, it’s important to note that accuracy also depends on the quality of data collection and the study design.

What is the relationship between sample size and statistical significance?

Sample size has a direct impact on statistical significance. Larger sample sizes increase the power of statistical tests, making it more likely to detect significant differences or effects. Smaller sample sizes may lack the power to identify subtle changes, potentially resulting in non-significant findings.

How does the standard error of the mean affect the sample size?

The standard error of the mean (se) is inversely related to sample size. As the sample size increases, the standard error decreases. A smaller standard error signifies a more precise estimation of the population mean. Therefore, a smaller standard error allows researchers to detect smaller differences or effects, reducing the required sample size for a given level of significance.

Is there an optimum sample size for statistical analysis?

Yes, there is an optimum sample size for statistical analysis. The determination of the optimum sample size depends on various factors, including the desired level of statistical power, the effect size researchers want to detect, and the variability within the population. Statistical formulas and power analyses can aid in calculating the ideal sample size for a specific research question.

Can a small sample size lead to misleading statistical conclusions?

Yes, a small sample size can potentially lead to misleading statistical conclusions. With a smaller sample, there is a higher chance of random variation impacting the results. Statistically insignificant findings in a small sample may not accurately reflect the true population characteristics. It’s important to interpret results from small samples with caution and consider the limitations imposed by the sample size.

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

• disquisition
• examination
• exploration
• inquisition
• investigation
• delve (into)
• inquire (into)
• investigate
• look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• research park

research and development

• translational research
• operations research
• marketing research
• market research
• oppo research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 29 Mar. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• Additional Help

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

• Next: Types of Research >>
• Last Updated: Dec 21, 2023 3:49 PM
• URL: https://guides.library.iit.edu/research_basics

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods.

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

14k Accesses

Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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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.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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

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

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

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

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

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

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Definition of research noun from the Oxford Advanced Learner's Dictionary

• scientific/medical/academic research
• They are raising money for cancer research.
• to do/conduct/undertake research
• I've done some research to find out the cheapest way of travelling there.
• research into something He has carried out extensive research into renewable energy sources.
• research on something/somebody Recent research on deaf children has produced some interesting findings about their speech.
• Research on animals has led to some important medical advances.
• according to research According to recent research, more people are going to the movies than ever before.
• Their latest research project will be funded by the government.
• Are you hoping to get a research grant ?
• a research fellow/assistant/scientist
• a research institute/centre/laboratory
• The research findings were published in the Journal of Environmental Quality.
• formulate/​advance a theory/​hypothesis
• build/​construct/​create/​develop a simple/​theoretical/​mathematical model
• develop/​establish/​provide/​use a theoretical/​conceptual framework
• advance/​argue/​develop the thesis that…
• explore an idea/​a concept/​a hypothesis
• make a prediction/​an inference
• base a prediction/​your calculations on something
• investigate/​evaluate/​accept/​challenge/​reject a theory/​hypothesis/​model
• design an experiment/​a questionnaire/​a study/​a test
• do research/​an experiment/​an analysis
• make observations/​measurements/​calculations
• carry out/​conduct/​perform an experiment/​a test/​a longitudinal study/​observations/​clinical trials
• run an experiment/​a simulation/​clinical trials
• repeat an experiment/​a test/​an analysis
• replicate a study/​the results/​the findings
• observe/​study/​examine/​investigate/​assess a pattern/​a process/​a behaviour
• fund/​support the research/​project/​study
• seek/​provide/​get/​secure funding for research
• collect/​gather/​extract data/​information
• yield data/​evidence/​similar findings/​the same results
• analyse/​examine the data/​soil samples/​a specimen
• consider/​compare/​interpret the results/​findings
• fit the data/​model
• confirm/​support/​verify a prediction/​a hypothesis/​the results/​the findings
• prove a conjecture/​hypothesis/​theorem
• draw/​make/​reach the same conclusions
• read/​review the records/​literature
• describe/​report an experiment/​a study
• present/​publish/​summarize the results/​findings
• present/​publish/​read/​review/​cite a paper in a scientific journal
• a debate about the ethics of embryonic stem cell research
• For his PhD he conducted field research in Indonesia.
• Further research is needed.
• Future research will hopefully give us a better understanding of how garlic works in the human body.
• Dr Babcock has conducted extensive research in the area of agricultural production.
• the funding of basic research in biology, chemistry and genetics
• Activists called for a ban on animal research.
• Work is under way to carry out more research on the gene.
• She returned to Jamaica to pursue her research on the African diaspora.
• Bad punctuation can slow down people's reading speeds, according to new research carried out at Bradford University.
• He focused his research on the economics of the interwar era.
• Most research in the field has concentrated on the effects on children.
• One paper based on research conducted at Oxford suggested that the drug may cause brain damage.
• Research demonstrates that women are more likely than men to provide social support to others.
• She's doing research on Czech music between the wars.
• The research does not support these conclusions.
• They are carrying out research into the natural flow patterns of water.
• They lack the resources to do their own research.
• What has their research shown?
• Funding for medical research has been cut quite dramatically.
• a startling piece of historical research
• pioneering research into skin disease
• They were the first to undertake pioneering research into the human genome.
• There is a significant amount of research into the effects of stress on junior doctors.
• He's done a lot of research into the background of this story.
• research which identifies the causes of depression
• spending on military research and development
• the research done in the 1950s that linked smoking with cancer
• The children are taking part in a research project to investigate technology-enabled learning.
• The Lancet published a research paper by the scientist at the centre of the controversy.
• Who is directing the group's research effort?
• She is chief of the clinical research program at McLean Hospital.
• James is a 24-year-old research student from Iowa.
• You will need to describe your research methods.
• Before a job interview, do your research and find out as much as you can about the company.
• Most academic research is carried out in universities.
• This is a piece of research that should be taken very seriously.
• This is an important area of research.
• There's a large body of research linking hypertension directly to impaired brain function.
• In the course of my researches, I came across some of my grandfather's old letters.
• demonstrate something
• find something
• identify something
• programme/​program
• research in
• research into
• research on
• an area of research
• focus your research on something
• somebody’s own research

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diligent and systematic inquiry or investigation into a subject in order to discover or revise facts, theories, applications, etc.: recent research in medicine.

a particular instance or piece of research.

to make researches; investigate carefully.

to make an extensive investigation into: to research a matter thoroughly.

Origin of research

Synonym study for research, other words for research, other words from research.

• re·search·a·ble, adjective
• re·search·er, re·search·ist, noun
• pro·re·search, adjective
• un·der·re·search, verb (used with object)

Words that may be confused with research

• re-search , research

Words Nearby research

• rescue grass
• rescue mission
• research and development
• research-intensive
• research library
• research park
• research quantum

Other definitions for re-search (2 of 2)

to search or search for again.

Origin of re-search

Words that may be confused with re-search.

Dictionary.com Unabridged Based on the Random House Unabridged Dictionary, © Random House, Inc. 2024

How to use research in a sentence

The duo spent the first year in research and engaging with farmers.

Dan Finn-Foley, head of energy storage at energy research firm Wood Mackenzie Power & Renewables, compared Google’s plan to ordering eggs for breakfast.

Users will give Deep Longevity the right to conduct anonymized research using their data as part of the app’s terms and conditions, Zhavoronkov said.

There’s also the Wilhelm Reich Museum, located at “Orgonon” in Rangeley, Maine, which was previously Reich’s estate—where he conducted questionable orgone research in the later years of his career.

When we started doing research on these topics, we were too focused on political institutions.

Have you tried to access the research that your tax dollars finance, almost all of which is kept behind a paywall?

Have a look at this telling research from Pew on blasphemy and apostasy laws around the world.

And Epstein continues to steer money toward universities to advance scientific research .

The research literature, too, asks these questions, and not without reason.

We also have a growing body of biological research showing that fathers, like mothers, are hard-wired to care for children.

We find by research that smoking was the most general mode of using tobacco in England when first introduced.

This class is composed frequently of persons of considerable learning, research and intelligence.

Speaking from recollection, it appears to be a work of some research ; but I cannot say how far it is to be relied on.

Thomas Pope Blount died; an eminent English writer and a man of great learning and research .

That was long before invention became a research department full of engineers.

British Dictionary definitions for research

/ ( rɪˈsɜːtʃ , ˈriːsɜːtʃ ) /

systematic investigation to establish facts or principles or to collect information on a subject

to carry out investigations into (a subject, problem, etc)

Derived forms of research

• researchable , adjective
• researcher , noun

Collins English Dictionary - Complete & Unabridged 2012 Digital Edition © William Collins Sons & Co. Ltd. 1979, 1986 © HarperCollins Publishers 1998, 2000, 2003, 2005, 2006, 2007, 2009, 2012

Definition of 'research'

Video: pronunciation of research

research in British English

Research in american english, examples of 'research' in a sentence research, cobuild collocations research, trends of research.

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• resealable bag
• research a book
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basic research noun

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What does the noun basic research mean?

There is one meaning in OED's entry for the noun basic research . See ‘Meaning & use’ for definition, usage, and quotation evidence.

How common is the noun basic research ?

Where does the noun basic research come from.

Earliest known use

The earliest known use of the noun basic research is in the 1900s.

OED's earliest evidence for basic research is from 1906, in 19th Annual Rep. Agric. Exper. Station Layfayette, Indiana .

basic research is formed within English, by compounding.

Etymons: basic adj. , research n. 1

Nearby entries

• basicerite, n. 1877–
• basichromatin, n. 1902–
• basicity, n. 1849–
• Basic Law, n. 1884–
• basic military training, n. 1915–
• basicness, n. 1857–
• basic number, n. 1932–
• basic-oxygen, adj. 1970–
• basic pay, n. 1916–
• basic rate, n. 1887–
• basic research, n. 1906–
• basic skill, n. 1909–
• basic training, n. 1898–
• basic wage, n. 1906–
• basidiospore, n. 1859–
• basidiosporous, adj. 1859–
• basidium, n. 1858–
• basifier, n. 1847–
• basifixed, adj. 1870–
• basifugal, adj. 1875–
• basifugally, adv. 1882–

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Meaning & use

Entry history for basic research, n..

Originally published as part of the entry for basic, adj. & n.¹

basic research, n. was first published in 2000.

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Citation details

Factsheet for basic research, n., browse entry.

• Cambridge Dictionary +Plus

Meaning of research in English

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• He has dedicated his life to scientific research.
• He emphasized that all the people taking part in the research were volunteers .
• The state of Michigan has endowed three institutes to do research for industry .
• I'd like to see the research that these recommendations are founded on.
• It took months of painstaking research to write the book .
• absorptive capacity
• dream something up
• modularization
• nanotechnology
• non-imitative
• operations research
• think outside the box idiom
• think something up
• uninventive
• study What do you plan on studying in college?
• major US She majored in philosophy at Harvard.
• cram She's cramming for her history exam.
• revise UK I'm revising for tomorrow's test.
• review US We're going to review for the test tomorrow night.
• research Scientists are researching possible new treatments for cancer.
• The amount of time and money being spent on researching this disease is pitiful .
• We are researching the reproduction of elephants .
• She researched a wide variety of jobs before deciding on law .
• He researches heart disease .
• The internet has reduced the amount of time it takes to research these subjects .
• adjudication
• analytically
• interpretable
• interpretive
• interpretively
• investigate
• reinvestigate
• reinvestigation
• risk assessment
• run over/through something
• run through something

You can also find related words, phrases, and synonyms in the topics:

Related word

Research | intermediate english, research | business english, examples of research, collocations with research.

These are words often used in combination with research .

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Translations of research

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Word of the Day

to change a document in order to deceive people

Sitting on the fence (Newspaper idioms)

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What Deathbed Visions Teach Us About Living

Researchers are documenting a phenomenon that seems to help the dying, as well as those they leave behind.

Credit... Photo illustration by Amy Friend

Supported by

By Phoebe Zerwick

• March 12, 2024

Chris Kerr was 12 when he first observed a deathbed vision. His memory of that summer in 1974 is blurred, but not the sense of mystery he felt at the bedside of his dying father. Throughout Kerr’s childhood in Toronto, his father, a surgeon, was too busy to spend much time with his son, except for an annual fishing trip they took, just the two of them, to the Canadian wilderness. Gaunt and weakened by cancer at 42, his father reached for the buttons on Kerr’s shirt, fiddled with them and said something about getting ready to catch the plane to their cabin in the woods. “I knew intuitively, I knew wherever he was, must be a good place because we were going fishing,” Kerr told me.

Listen to this article, read by Samantha Desz

Open this article in the New York Times Audio app on iOS.

As he moved to touch his father, Kerr felt a hand on his shoulder. A priest had followed him into the hospital room and was now leading him away, telling him his father was delusional. Kerr’s father died early the next morning. Kerr now calls what he witnessed an end-of-life vision. His father wasn’t delusional, he believes. His mind was taking him to a time and place where he and his son could be together, in the wilds of northern Canada. And the priest, he feels, made a mistake, one that many other caregivers make, of dismissing the moment as a break with reality, as something from which the boy required protection.

It would be more than 40 years before Kerr felt compelled to speak about that evening in the hospital room. He had followed his father, and three generations before him, into medicine and was working at Hospice & Palliative Care Buffalo, where he was the chief medical officer and conducted research on end-of-life visions. It wasn’t until he gave a TEDx Talk in 2015 that he shared the story of his father’s death. Pacing the stage in the sport coat he always wears, he told the audience: “My point here is, I didn’t choose this topic of dying. I feel it has chosen or followed me.” He went on: “When I was present at the bedside of the dying, I was confronted by what I had seen and tried so hard to forget from my childhood. I saw dying patients reaching and calling out to mothers, and to fathers, and to children, many of whom hadn’t been seen for many years. But what was remarkable was so many of them looked at peace.”

The talk received millions of views and thousands of comments, many from nurses grateful that someone in the medical field validated what they have long understood. Others, too, posted personal stories of having witnessed loved ones’ visions in their final days. For them, Kerr’s message was a kind of confirmation of something they instinctively knew — that deathbed visions are real, can provide comfort, even heal past trauma. That they can, in some cases, feel transcendent. That our minds are capable of conjuring images that help us, at the end, make sense of our lives.

Nothing in Kerr’s medical training prepared him for his first shift at Hospice Buffalo one Saturday morning in the spring of 1999. He had earned a degree from the Medical College of Ohio while working on a Ph.D. in neurobiology. After a residency in internal medicine, Kerr started a fellowship in cardiology in Buffalo. To earn extra money to support his wife and two young daughters, he took a part-time job with Hospice Buffalo. Until then, Kerr had worked in the conventional medical system, focused on patients who were often tethered to machines or heavily medicated. If they recounted visions, he had no time to listen. But in the quiet of Hospice, Kerr found himself in the presence of something he hadn’t seen since his father’s death: patients who spoke of people and places visible only to them. “So just like with my father, there’s just this feeling of reverence, of something that wasn’t understood but certainly felt,” he says.

During one of his shifts, Kerr was checking on a 70-year-old woman named Mary, whose grown children had gathered in her room, drinking wine to lighten the mood. Without warning, Kerr remembers, Mary sat up in her bed and crossed her arms at her chest. “Danny,” she cooed, kissing and cuddling a baby only she could see. At first, her children were confused. There was no Danny in the family, no baby in their mother’s arms. But they could sense that whatever their mother was experiencing brought her a sense of calm. Kerr later learned that long before her four children were born, Mary lost a baby in childbirth. She never spoke of it with her children, but now she was, through a vision, seemingly addressing that loss.

In observing Mary’s final days at Hospice, Kerr found his calling. “I was disillusioned by the assembly-line nature of medicine,” Kerr told me. “This felt like a more humane and dignified model of care.” He quit cardiology to work full time at the bedsides of dying patients. Many of them described visions that drew from their lives and seemed to hold meaning, unlike hallucinations resulting from medication, or delusional, incoherent thinking, which can also occur at the end of life. But Kerr couldn’t persuade other doctors, even young residents making the rounds with him at Hospice, of their value. They wanted scientific proof.

At the time, only a handful of published medical studies had documented deathbed visions, and they largely relied on secondhand reports from doctors and other caregivers rather than accounts from patients themselves. On a flight home from a conference, Kerr outlined a study of his own, and in 2010, a research fellow, Anne Banas, signed on to conduct it with him. Like Kerr, Banas had a family member who, before his death, experienced visions — a grandfather who imagined himself in a train station with his brothers.

The study wasn’t designed to answer how these visions differ neurologically from hallucinations or delusions. Rather, Kerr saw his role as chronicler of his patients’ experiences. Borrowing from social-science research methods, Kerr, Banas and their colleagues based their study on daily interviews with patients in the 22-bed inpatient unit at the Hospice campus in the hope of capturing the frequency and varied subject matter of their visions. Patients were screened to ensure that they were lucid and not in a confused or delirious state. The research, published in 2014 in The Journal of Palliative Medicine, found that visions are far more common and frequent than other researchers had found, with an astonishing 88 percent of patients reporting at least one vision. (Later studies in Japan, India, Sweden and Australia confirm that visions are common. The percentages range from about 20 to 80 percent, though a majority of these studies rely on interviews with caregivers and not patients.)

In the last 10 years, Kerr has hired a permanent research team who expanded the studies to include interviews with patients receiving hospice care at home and with their families, deepening the researchers’ understanding of the variety and profundity of these visions. They can occur while patients are asleep or fully conscious. Dead family members figure most prominently, and by contrast, visions involving religious themes are exceedingly rare. Patients often relive seminal moments from their lives, including joyful experiences of falling in love and painful ones of rejection. Some dream of the unresolved tasks of daily life, like paying bills or raising children. Visions also entail past or imagined journeys — whether long car trips or short walks to school. Regardless of the subject matter, the visions, patients say, feel real and entirely unique compared with anything else they’ve ever experienced. They can begin days, even weeks, before death. Most significant, as people near the end of their lives, the frequency of visions increases, further centering on deceased people or pets. It is these final visions that provide patients, and their loved ones, with profound meaning and solace.

Kerr’s latest research is focused on the emotional transformation he has often observed in patients who experience such visions. The first in this series of studies, published in 2019, measured psychological and spiritual growth among two groups of hospice patients: those who had visions and a control group of those who did not. Patients rated their agreement with statements including, “I changed my priorities about what is important in life,” or “I have a better understanding of spiritual matters.” Those who experienced end-of-life visions agreed more strongly with those statements, suggesting that the visions sparked inner change even at the end of life. “It’s the most remarkable of our studies,” Kerr told me. “It highlights the paradox of dying, that while there is physical deterioration, they are growing and finding meaning. It highlights what patients are telling us, that they are being put back together.”

In the many conversations Kerr and I have had over the past year, the contradiction between medicine’s demand for evidence and the ineffable quality of his patients’ experiences came up repeatedly. He was first struck by this tension about a year before the publication of his first study, during a visit with a World War II veteran named John who was tormented throughout his life by nightmares that took him back to the beaches of Normandy on D-Day. John had been part of a rescue mission to bring wounded soldiers to England by ship and leave those too far gone to die. The nightmares continued through his dying days, until he dreamed of being discharged from the Army. In a second dream, a fallen soldier appeared to John to tell him that his comrades would soon come to “get” him. The nightmares ended after that.

Kerr has been nagged ever since by the inadequacy of science, and of language, to fully capture the mysteries of the mind. “We were so caught up in trying to quantify and give structure to something so deeply spiritual, and really, we were just bystanders, witnesses to this,” he says. “It feels a little small to be filling in forms when you’re looking at a 90-something-year-old veteran who is back in time 70 years having an experience you can’t even understand.” When Kerr talks about his research at conferences, nurses tend to nod their heads in approval; doctors roll their eyes in disbelief. He finds that skeptics often understand the research best when they watch taped interviews with patients.

What’s striking about this footage, which dates back to Kerr’s early work in 2008, is not so much the content of the visions but rather the patients’ demeanor. “There’s an absence of fear,” Kerr says. A teenage girl’s face lights up as she describes a dream in which she and her deceased aunt were in a castle playing with Barbie dolls. A man dying of cancer talks about his wife, who died several years earlier and who comes to him in his dreams, always in blue. She waves. She smiles. That’s it. But in the moment, he seems to be transported to another time or place.

Kerr has often observed that in the very end, dying people lose interest in the activities that preoccupied them in life and turn toward those they love. As to why, Kerr can only speculate. In his 2020 book, “Death Is but a Dream,” he concludes that the love his patients find in dying often brings them to a place that some call enlightenment and others call God. “Time seems to vanish,” he told me. “The people who loved you well, secured you and contributed to who you are are still accessible at a spiritual and psychological level.”

That was the case with Connor O’Neil, who died at the age of 10 in 2022 and whose parents Kerr and I visited in their home. They told us that just two days before his death, their son called out the name of a family friend who, without the boy’s knowledge, had just died. “Do you know where you are?” Connor’s mother asked. “Heaven,” the boy replied. Connor had barely spoken in days or moved without help, but in that moment, he sat up under his own strength and threw his arms around her neck. “Mommy, I love you,” he said.

Kerr’s research finds that such moments, which transcend the often-painful physical decline in the last days of life, help parents like the O’Neils and other relatives grieve even unfathomable loss. “I don’t know where I would be without that closure, or that gift that was given to us,” Connor’s father told us. “It’s hard enough with it.” As Kerr explains, “It’s the difference between being wounded and soothed.”

In June, I visited the adult daughter of a patient who died at home just days earlier. We sat in her mother’s living room, looking out on the patio and bird feeders that had given the mother so much joy. Three days before her mother’s death, the daughter was straightening up the room when her mother began to speak more lucidly than she had in days. The daughter crawled into her mother’s bed, held her hand and listened. Her mother first spoke to the daughter’s father, whom she could see in the far corner of the room, handsome as ever. She then started speaking with her second husband, visible only to her, yet real enough for the daughter to ask whether he was smoking his pipe. “Can’t you smell it?” her mother replied. Even in the retelling, the moment felt sacred. “I will never, ever forget it,” the daughter told me. “It was so beautiful.”

I also met one of Banas’s patients, Peggy Haloski, who had enrolled in hospice for home care services just days earlier, after doctors at the cancer hospital in Buffalo found blood clots throughout her body, a sign that the yearlong treatment had stopped working. It was time for her husband, Stephen, to keep her comfortable at home, with their two greyhounds.

Stephen led Banas and me to the family room, where Peggy lay on the couch. Banas knelt on the floor, checked her patient’s catheter, reduced her prescriptions so there were fewer pills for her to swallow every day and ordered a numbing cream for pain in her tailbone. She also asked about her visions.

The nurse on call that weekend witnessed Peggy speaking with her dead mother.

“She was standing over here,” Peggy told Banas, gesturing toward the corner of the room.

“Was that the only time you saw her?” Banas asked.

“Do you think you’ll be seeing her more?”

“I will. I will, considering what’s going on.”

Peggy sank deeper into the couch and closed her eyes, recounting another visit from the dead, this time by the first greyhound she and Stephen adopted. “I’m at peace with everybody. I’m happy,” she said. “It’s not time yet. I know it’s not time, but it’s coming.”

When my mother, Chloe Zerwick, was dying in 2018, I had never heard of end-of-life visions. I was acting on intuition when her caregivers started telling me about what we were then calling hallucinations. Mom was 95 and living in her Hudson Valley home under hospice care, with lung disease and congestive heart failure, barely able to leave her bed. The hospice doctor prescribed an opioid for pain and put her on antipsychotic and anti-anxiety medicines to tame the so-called hallucinations he worried were preventing her from sleeping. It is possible that some of these medications caused Mom’s visions, but as Kerr has explained, drug-induced hallucinations do not rule out naturally occurring visions. They can coexist.

In my mother’s case, I inherently understood that her imaginary life was something to honor. I knew what medicine-induced hallucinations looked and felt like. About 10 years before her death, Mom fell and injured her spine. Doctors in the local hospital put her on an opioid to control the pain, which left her acting like a different person. There were spiders crawling on the hospital wall, she said. She mistook her roommate’s bed for a train platform. Worse, she denied that I loved her or ever did. Once we took her off the medicine, the hallucinations vanished.

The visions she was having at the end of her life were entirely different; they were connected to the long life she had led and brought a deep sense of comfort and delight. “You know, for the first time in my life I have no worries,” she told me. I remember feeling a weight lift. After more than a decade of failing health, she seemed to have found a sense of peace.

The day before her death, as her breathing became more labored, Mom made an announcement: “I have a new leader,” she said.

“Who is that?” I asked.

“Mark. He’s going to take me to the other side.”

She was speaking of my husband, alive and well back home in North Carolina.

“That’s great, Mom, except that I need him here with me,” I replied. “Do you think he can do both?”

“Oh, yes. He’s very capable.”

That evening, Mom was struggling again to breathe. “I’m thinking of the next world,” she said, and of my husband, who would lead her there. The caregiver on duty for the night and I sat at her bedside as Mom’s oxygen level fell from 68 to 63 to 52 and kept dropping until she died the next morning. My mother was not a brave person in the traditional sense of the word. She was afraid of snakes, the subway platform and any hint of pain. But she faced her death, confident that a man who loves her daughter would guide her to whatever lay ahead.

“Do you think it will happen to you?” she asked me at one point about her dreaming life.

“Maybe it’s genetic,” I replied, not knowing, as I do now, that these experiences are part of what may await us all.

Phoebe Zerwick, the author of “Beyond Innocence: The Life Sentence of Darryl Hunt,” is a North Carolina-based journalist. She teaches journalism and writing at Wake Forest University, where she directs the journalism program. Amy Friend is an artist in Canada whose work focuses on history, time, land-memory, dust, oceans and our connection to the universe.

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The ‘Colorblindness’ Trap: Nikole Hannah-Jones examines how the fall of affirmative action may be viewed as part of a 50-year campaign to undermine the progress of the civil rights movement .

Deathbed Visions: Researchers are documenting the illusions seen by the dying , a phenomenon that seems to help them, as well as those they leave behind.

The Mad Perfumer of Parma: Hilde Soliani, the creator of fantastical perfumes, makes feral scents that evoke everything from oysters to opera houses .

Mona Island’s Terrifying Allure: Here’s why immigrants, seekers and pilgrims have been drawn for centuries to the treacherous shores of the remote island near Puerto Rico .

Creature Comforts: How exactly did pets take over our world? A writer spent a week at some luxury dog “hotels”  with his goldendoodle to find out.

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• Compassionate use

Compassionate use is a treatment option that allows the use of an unauthorised medicine. Under strict conditions, products in development can be made available to groups of patients who have a disease with no satisfactory authorised therapies and who cannot enter clinical trials.

The European Medicines Agency (EMA) provides recommendations through the Committee for Medicinal Products for Human Use (CHMP) , but these do not create a legal framework. Compassionate use programmes are coordinated and implemented by Member States, which set their own rules and procedures.

Established by Article 83 of Regulation (EC) No 726/2004 , this tool is designed to:

• facilitate and improve access to compassionate use programmes by patients in the EU;
• favour a common approach regarding the conditions of use, the conditions for distribution and the patients targeted for the compassionate use of unauthorised new medicines;
• increase transparency between Member States in terms of treatment availability.

These programmes are only put in place if the medicine is expected to help patients with life-threatening, long-lasting or seriously debilitating illnesses , which cannot be treated satisfactorily with any currently authorised medicine.

The medicine must be undergoing clinical trials or have entered the marketing-authorisation application process and while early studies will generally have been completed, its safety profile and dosage guidelines may not be fully established.

How to request an opinion

National competent authorities can ask EMA for an opinion on how to administer, distribute and use certain medicines for compassionate use. The CHMP also identifies which patients would benefit, and Member States should take note of these recommendations when making decisions.

Manufacturers and marketing-authorisation applicants should not contact EMA to request an opinion, but they may wish to inform the Agency of applications underway at national level. National competent authorities will inform the Agency if they are making a product available to a group of patients for compassionate use.

Comparison to individual basis treatment

Compassionate use should not be confused with 'named-patient basis' treatments, which see doctors obtain medicines directly from manufacturers before authorisation. This is done on an individual basis under the direct responsibility of the doctor, and the Agency does not need to be informed.

In general, medicines that are not yet authorised are first made available through clinical trials and patients should always be considered for inclusion in trials before being offered compassionate use programmes.

Compassionate use recommendations

EMA's recommendations cover how a medicine should be used in compassionate use programmes across the EU, and the type of patient who may benefit from treatment.

EMA does not update its recommendations after a medicine receives marketing authorisation , as all relevant information on the medicine's use is available in its European public assessment report ( EPAR ). However, compassionate use programmes may continue in certain Member States until the medicine becomes available on the market. 

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What is artificial general intelligence (AGI)?

You’ve read the think pieces. AI—in particular, the generative AI (gen AI) breakthroughs achieved in the past year or so—is poised to revolutionize not just the way we create content but the very makeup of our economies and societies as a whole. But although gen AI tools such as ChatGPT may seem like a great leap forward, in reality they are just a step in the direction of an even greater breakthrough: artificial general intelligence, or AGI.

Get to know and directly engage with senior McKinsey experts on AGI

Aamer Baig is a senior partner in McKinsey’s Chicago office; Federico Berruti is a partner in the Toronto office; Ben Ellencweig is a senior partner in the Stamford, Connecticut, office; Damian Lewandowski is a consultant in the Miami office; Roger Roberts is a partner in the Bay Area office, where Lareina Yee is a senior partner;  Alex Singla  is a senior partner in the Chicago office and the global leader of QuantumBlack, AI by McKinsey;  Kate Smaje  and Alex Sukharevsky  are senior partners in the London office;   Jonathan Tilley is a partner in the Southern California office; and Rodney Zemmel is a senior partner in the New York office.

AGI is AI with capabilities that rival those of a human . While purely theoretical at this stage, someday AGI may replicate human-like cognitive abilities including reasoning, problem solving, perception, learning, and language comprehension. When AI’s abilities are indistinguishable from those of a human, it will have passed what is known as the Turing test , first proposed by 20th-century computer scientist Alan Turing.

But let’s not get ahead of ourselves. AI has made significant strides in recent years, but no AI tool to date has passed the Turing test. We’re still far from reaching a point where AI tools can understand, communicate, and act with the same nuance and sensitivity of a human—and, critically, understand the meaning behind it. Most researchers and academics believe we are decades away from realizing AGI; a few even predict we won’t see AGI this century (or ever). Rodney Brooks, a roboticist at the Massachusetts Institute of Technology and cofounder of iRobot, believes AGI won’t arrive until the year 2300 .

If you’re thinking that AI already seems pretty smart, that’s understandable. We’ve seen gen AI  do remarkable things in recent years, from writing code to composing sonnets in seconds. But there’s a critical difference between AI and AGI. Although the latest gen AI technologies, including ChatGPT, DALL-E, and others, have been hogging headlines, they are essentially prediction machines—albeit very good ones. In other words, they can predict, with a high degree of accuracy, the answer to a specific prompt because they’ve been trained on huge amounts of data. This is impressive, but it’s not at a human level of performance in terms of creativity, logical reasoning, sensory perception, and other capabilities . By contrast, AGI tools could feature cognitive and emotional abilities (like empathy) indistinguishable from those of a human. Depending on your definition of AGI, they might even be capable of consciously grasping the meaning behind what they’re doing.

The timing of AGI’s emergence is uncertain. But when it does arrive—and it likely will at some point—it’s going to be a very big deal for every aspect of our lives, businesses, and societies. Executives can begin working now to better understand the path to machines achieving human-level intelligence and making the transition to a more automated world.

Learn more about QuantumBlack, AI by McKinsey .

What is needed for AI to become AGI?

Here are eight capabilities AI needs to master before achieving AGI. Click each card to learn more.

How will people access AGI tools?

Today, most people engage with AI in the same ways they’ve accessed digital power for years: via 2D screens such as laptops, smartphones, and TVs. The future will probably look a lot different. Some of the brightest minds (and biggest budgets) in tech are devoting themselves to figuring out how we’ll access AI (and possibly AGI) in the future. One example you’re likely familiar with is augmented reality and virtual reality headsets , through which users experience an immersive virtual world . Another example would be humans accessing the AI world through implanted neurons in the brain. This might sound like something out of a sci-fi novel, but it’s not. In January 2024, Neuralink implanted a chip in a human brain, with the goal of allowing the human to control a phone or computer purely by thought.

A final mode of interaction with AI seems ripped from sci-fi as well: robots. These can take the form of mechanized limbs connected to humans or machine bases or even programmed humanoid robots.

What is a robot and what types of robots are there?

The simplest definition of a robot is a machine that can perform tasks on its own or with minimal assistance from humans. The most sophisticated robots can also interact with their surroundings.

Programmable robots have been operational since the 1950s. McKinsey estimates that 3.5 million robots are currently in use, with 550,000 more deployed every year. But while programmable robots are more commonplace than ever in the workforce, they have a long way to go before they outnumber their human counterparts. The Republic of Korea, home to the world’s highest density of robots, still employs 100 times as many humans as robots.

Introducing McKinsey Explainers : Direct answers to complex questions

But as hardware and software limitations become increasingly surmountable, companies that manufacture robots are beginning to program units with new AI tools and techniques. These dramatically improve robots’ ability to perform tasks typically handled by humans, including walking, sensing, communicating, and manipulating objects. In May 2023, Sanctuary AI, for example, launched Phoenix, a bipedal humanoid robot that stands 5’ 7” tall, lifts objects weighing as much as 55 pounds, and travels three miles per hour—not to mention it also folds clothes, stocks shelves, and works a register.

As we edge closer to AGI, we can expect increasingly sophisticated AI tools and techniques to be programmed into robots of all kinds. Here are a few categories of robots that are currently operational:

• Stand-alone autonomous industrial robots : Equipped with sensors and computer systems to navigate their surroundings and interact with other machines, these robots are critical components of the modern automated manufacturing industry.
• Collaborative robots : Also known as cobots, these robots are specifically engineered to operate in collaboration with humans in a shared environment. Their primary purpose is to alleviate repetitive or hazardous tasks. These types of robots are already being used in environments such as restaurant kitchens and more.
• Mobile robots : Utilizing wheels as their primary means of movement, mobile robots are commonly used for materials handling in warehouses and factories. The military also uses these machines for various purposes, such as reconnaissance and bomb disposal.
• Human–hybrid robots : These robots have both human and robotic features. This could include a robot with an appearance, movement capabilities, or cognition that resemble those of a human, or a human with a robotic limb or even a brain implant.
• Humanoids or androids : These robots are designed to emulate the appearance, movement, communicative abilities, and emotions of humans while continuously enhancing their cognitive capabilities via deep learning models. In other words, humanoid robots will think like a human, move like a human, and look like a human.

What advances could speed up the development of AGI?

Advances in algorithms, computing, and data  have brought about the recent acceleration of AI. We can get a sense of what the future may hold by looking at these three capabilities:

Algorithmic advances and new robotics approaches . We may need entirely new approaches to algorithms and robots to achieve AGI. One way researchers are thinking about this is by exploring the concept of embodied cognition. The idea is that robots will need to learn very quickly from their environments through a multitude of senses, just like humans do when they’re very young. Similarly, to develop cognition in the same way humans do, robots will need to experience the physical world like we do (because we’ve designed our spaces based on how our bodies and minds work).

The latest AI-based robot systems are using gen AI technologies including large language models (LLMs) and large behavior models (LBMs). LLMs give robots advanced natural-language-processing capabilities like what we’ve seen with generative AI models and other LLM-enabled tools. LBMs allow robots to emulate human actions and movements. These models are created by training AI on large data sets of observed human actions and movements. Ultimately, these models could allow robots to perform a wide range of activities with limited task-specific training.

A real advance would be to develop new AI systems that start out with a certain level of built-in knowledge, just like a baby fawn knows how to stand and feed without being taught. It’s possible that the recent success of deep-learning-based AI systems may have drawn research attention away from the more fundamental cognitive work required to make progress toward AGI.

• Computing advancements. Graphics processing units (GPUs) have made the major AI advances of the past few years possible . Here’s why. For one, GPUs are designed to handle multiple tasks related to visual data simultaneously, including rendering images, videos, and graphics-related computations. Their efficiency at handling massive amounts of visual data makes them useful in training complex neural networks. They also have a high memory bandwidth, meaning faster data transfer. Before AGI can be achieved, similar significant advancements will need to be made in computing infrastructure. Quantum computing  is touted as one way of achieving this. However, today’s quantum computers, while powerful, aren’t yet ready for everyday applications. But once they are, they could play a role in the achievement of AGI.

Growth in data volume and new sources of data . Some experts believe 5G  mobile infrastructure could bring about a significant increase in data. That’s because the technology could power a surge in connected devices, or the Internet of Things . But, for a variety of reasons, we think most of the benefits of 5G have already appeared . For AGI to be achieved, there will need to be another catalyst for a huge increase in data volume.

New robotics approaches could yield new sources of training data. Placing human-like robots among us could allow companies to mine large sets of data that mimic our own senses to help the robots train themselves. Advanced self-driving cars are one example: data is being collected from cars that are already on the roads, so these vehicles are acting as a training set for future self-driving cars.

What can executives do about AGI?

AGI is still decades away, at the very least. But AI is here to stay—and it is advancing extremely quickly. Smart leaders can think about how to respond to the real progress that’s happening, as well as how to prepare for the automated future. Here are a few things to consider:

• Stay informed about developments in AI and AGI . Connect with start-ups and develop a framework for tracking progress in AGI that is relevant to your business. Also, start to think about the right governance, conditions, and boundaries for success within your business and communities.
• Invest in AI now . “The cost of doing nothing,” says McKinsey senior partner Nicolai Müller , “is just too high  because everybody has this at the top of their agenda. I think it’s the one topic that every management board  has looked into, that every CEO  has explored across all regions and industries.” The organizations that get it right now will be poised to win in the coming era.
• Continue to place humans at the center . Invest in human–machine interfaces, or “human in the loop” technologies that augment human intelligence. People at all levels of an organization need training and support to thrive in an increasingly automated world. AI is just the latest tool to help individuals and companies alike boost their efficiency.
• Consider the ethical and security implications . This should include addressing cybersecurity , data privacy, and algorithm bias.
• Build a strong foundation of data, talent, and capabilities . AI runs on data; having a strong foundation of high-quality data is critical to its success.
• Organize your workers for new economies of scale and skill . Yesterday’s rigid organizational structures and operating models aren’t suited to the reality of rapidly advancing AI. One way to address this is by instituting flow-to-the-work models, where people can move seamlessly between initiatives and groups.
• Place small bets to preserve strategic options in areas of your business that are exposed to AI developments . For example, consider investing in technology firms that are pursuing ambitious AI research and development projects in your industry. Not all these bets will necessarily pay off, but they could help hedge some of the existential risk your business may face in the future.

Learn more about QuantumBlack, AI by McKinsey . And check out AI-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ Generative AI in operations: Capturing the value ,” January 3, 2024, Marie El Hoyek and  Nicolai Müller
• “ The economic potential of generative AI: The next productivity frontier ,” June 14, 2023, Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel
• “ What every CEO should know about generative AI ,” May 12, 2023, Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya
• “ An executive primer on artificial general intelligence ,” April 29, 2020, Federico Berruti , Pieter Nel, and Rob Whiteman
• “ Notes from the AI frontier: Applications and value of deep learning ,” April 17, 2018, Michael Chui , James Manyika , Mehdi Miremadi, Nicolaus Henke, Rita Chung, Pieter Nel, and Sankalp Malhotra
• “ Augmented and virtual reality: The promise and peril of immersive technologies ,” October 3, 2017, Stefan Hall and Ryo Takahashi

Want to know more about artificial general intelligence (AGI)?

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An executive primer on artificial general intelligence

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Dogs Can Understand the Words for Several Objects, Such as Toys and Leashes, Study Finds

Your dog may know the word “ball” is associated with their favorite round squishy toy, according to new research that measured brain waves

Daily Correspondent

It’s no secret that dogs can learn to associate certain words with specific behaviors, such as “sit” and “stay.” Now, new research published last week in the journal Current Biology suggests our four-legged friends can also link words with objects, including “ball” and “Frisbee.”

The findings likely won’t come as a surprise to any pet parent who has ever asked their dog to “Go get your toy!” and, a few seconds later, been presented with a slobbery rope or holey stuffed animal.

But they do offer new insights into the cognitive abilities of man’s best friend—and they suggest that “dogs may understand more than they show,” study lead author Marianna Boros , an ethologist at Hungary’s Eötvös Loránd University, says to New Scientist ’s James Woodford.

The study also provides the “first neural evidence for object word knowledge in a non-human animal,” the researchers write in the paper.

Researchers asked 18 dog owners to bring their pups into the lab, along with five objects each dog was familiar with—things like leashes, Frisbees, slippers and toys. The scientists hooked the dogs up to an electroencephalogram (EEG) machine using non-invasive scalp sensors to measure their brain activity.

For the experiment, each dog’s owner said aloud the name of an object, then presented the dog with either that named object or a different one. For example, in some scenarios, the human said “ball” and then showed the dog a ball, while in other cases, the human said “ball” and presented the dog with a Frisbee.

When the scientists analyzed the EEG recordings, they saw different patterns of brain activity depending on whether the object matched or didn’t match the spoken word. The differences were greater for words the dogs knew especially well.

The patterns were similar to what has been observed in humans during past studies and suggest that dogs are capable of linking words with specific objects.

“The dog was thinking, ‘I heard the word, now the object needs to come,’” says study co-author Lilla Magyari , a cognitive neuroscientist at the University of Stavanger in Norway, to the Los Angeles Times ’ Karen Kaplan.

But when their owners presented them with a mismatched object instead, their brains had to do a little extra processing to make sense of the difference—and that slight shift showed up on the EEG.

The study involved a variety of dog breeds—such as border collies, vizslas, schnauzers and mixed breeds—but the researchers found no differences in language ability among the different breeds.

The findings build upon a 2011 study describing a border collie named Chaser who learned the names of more than 1,000 objects , after three years of training. But the researchers say their experiments indicate the “capacity is there in all dogs,” Boros tells the Guardian ’s Ian Sample.

“It doesn’t matter how many object words a dog understands—known words activate mental representations anyway, suggesting that this ability is generally present in dogs and not just in some exceptional individuals who know the names of many objects,” Boros says in a statement .

Why, then, do some dogs refuse to fetch a ball or stuffed toy at their owner’s command? It likely has more to do with their desire to do so, rather than their ability to understand what’s being asked of them.

“It might be that the dogs don’t really care enough about the game of ‘fetch this particular thing’ to play along with the way we’ve been training and testing them so far,” says Holly Root-Gutteridge , a dog behavior researcher at the University of Lincoln in England who was not involved in the research, to the Guardian . “Your dog may understand what you’re saying but choose not to act.”

Moving forward, researchers are curious to know whether other mammals can link words with objects, or whether this ability is unique to dogs. Future studies might also explore whether dogs understand that words like “ball” can apply to multiple different objects, not just one specific ball they’re familiar with.

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Sarah Kuta | READ MORE

Sarah Kuta is a writer and editor based in Longmont, Colorado. She covers history, science, travel, food and beverage, sustainability, economics and other topics.

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It’s Time to Reconceptualize What “Imposter Syndrome” Means for People of Color

• Kevin Cokley

How racism, bias, and imposter feelings are intertwined.

The recent pushback against the imposter phenomenon in the media has largely focused on how and why it’s inappropriate for people of color. In this article, the author argues that, while there is merit to these arguments, getting rid of the idea entirely for Black students and workers is a disservice. Instead, he recommends reconceptualizing the term to include new research on how imposterism affects people of color, and urges organizations to better understand how racism, bias, and imposter feelings are intertwined.

Over the past few years, there has been increased attention paid to the imposter phenomenon (a.k.a., imposter syndrome) in the media. Its popularity is understandable given that it’s an intuitive, common-sense concept about a tremendously relatable topic: feeling like a phony on the job. It’s also, at least according to recent review of the literature , fairly common: up to 80% of people have experienced imposter feelings.

• Kevin Cokley is the University Diversity and Social Transformation Professor and Professor of Psychology at the University of Michigan where he serves as Associate Chair of Diversity Initiatives. He is editor of the forthcoming book The Impostor Phenomenon: Psychological Theory, Research, and Interventions . His Hidden Brain podcast “Success 2.0: The Psychology of Self-Doubt” addresses the corrosive effects of self-doubt and how we can turn that negative voice in our heads into an ally.

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Watching a solar eclipse without the right filters can cause eye damage. Here's why

Nell Greenfieldboyce

A woman watches an annular solar eclipse on October 14, 2023 using special solar filter glasses at the National Autonomous University of Mexico. Carlos Tischler/ Eyepix Group/Future Publishing via Getty Images hide caption

A woman watches an annular solar eclipse on October 14, 2023 using special solar filter glasses at the National Autonomous University of Mexico.

On April 8, as millions of people try to watch a solar eclipse sweep over North America, eye doctors across the United States will be on high alert.

That's because, while a solar eclipse is a stunning celestial event, it can also be dangerous. Looking at any part of the exposed sun without the right kind of protection can permanently injure the eye's light-sensitive retina.

And if past eclipses are prologue, it's likely that some eclipse-gazers will show up at doctors' offices with significant eye damage.

For April's eclipse, going from 'meh' to 'OMG' might mean just driving across town

In 2017, during the solar eclipse seen across the United States, that happened to multiple people despite abundant media coverage about the danger of looking at the sun when it is anything less than fully and completely covered by the moon.

In New York City, for example, one young woman came to the New York Eye and Ear Infirmary of Mount Sinai, complaining of blurred and distorted vision.

She had peeked up at the crescent sun without eyewear at first, then looked at it longer while wearing what she thought were appropriate eclipse glasses.

Everything you need to know about solar eclipse glasses before April 8

"But the problem was she was handed glasses from someone else," says ophthalmologist Avnish Deobhakta , so she didn't know if the eyewear really met safety standards .

Doctors found a permanent, crescent-shaped wound on her retina; there's no treatment for that kind of injury, which is similar to the kind of light-induced damage caused by pointing a laser into the eye.

Other eclipse-related eye injuries were reported in California and Utah .

Given that more than 150 million people directly viewed either a partial eclipse or a total solar eclipse, however, the number who suffered eye problems may seem relatively small.

Plan to watch the eclipse from a wild mountain summit? Be ready for harsh conditions

"We've got less than 100 cases across Canada and the U.S.," says Ralph Chou , an eclipse eye safety expert with the University of Waterloo in Canada.

But no one knows for sure how many people damaged their eyes in 2017, he says, because not every case gets written up for a medical journal, and people may not seek help for less severe vision troubles.

"A lot of them, if they actually happened, were probably relatively minor and, you know, they resolved on their own within weeks or months," says Chou, who says that about half of those who experience significant blurring on the day after an eclipse will recover almost completely.

Some of that recovery may just be the brain learning to compensate and "fill in" the blanks, says Deobhakta, who notes that "there's two eyes, and often there's asymmetric injury. Your brain kind of gets used to it."

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He notes that there are ways to enjoy the eclipse without looking up at all; everyday household objects like colanders allow you to create pinhole projectors that let you watch an image of the sun becoming more and more crescent-shaped.

"My advice is to not look at the sun, because you may not realize that it is affecting your retina. It does not hurt. It doesn't burn at the time. It's not as if you feel it," says Deobhakta.

If you do choose to look up at the sun when it is partially eclipsed, says Deobhakta, "make sure you really are sure that you have the standard glasses that have the right filters."

The American Astronomical Society has a list of vetted suppliers .

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If you still have reliable eclipse viewers from 2017 that are in good condition, those should still work fine, says Chou.

He notes that eclipse viewers usually have a "best by" date on them, but that is to satisfy European regulations related to personal protective equipment.

"It's essentially meaningless because the filters do not age," says Chou. "If you've taken good care of the viewers from 2017, they haven't been crushed or folded or whatever to damage the mountings, then they're perfectly safe to use for this eclipse."

Despite the warnings, some people try to glimpse the partially-eclipsed sun without eye protection, thinking that a quick look won't cause any harm. While an initial glance at the sun may not cause lasting damage, says Chou, repeated peeks do add up.

"At some point, you may tip yourself over the critical threshold," says Chou. "Unfortunately, you don't realize that until far too late."

The eye damage only becomes apparent hours after it occurs. Typically, people wake up the morning after observing an eclipse and see a spot of extreme fuzziness in the center of their field of vision.

There is one time when it's safe to look up at the sun with the naked eye, experts say, and that's when the sun is totally covered by the moon.

This eclipse phase is only visible from the so-called " path of totality ," a stretch of land from Texas to Maine. And the experience of totality doesn't last long — up to four and a half minutes or so, depending on your location.

When the sun is 100% obscured, the sky abruptly darkens and the once-bright sun becomes a dark circle surrounded by a ghostly white ring called the corona.

If people wear super-dark eclipse eyewear during these dramatic moments, they'll miss the whole show.

"People get so concerned to not hurt their eyes, which of course is super important, that they don't take their glasses off when the moon completely covers the sun," says Laura Peticolas , a space physicist at Sonoma State University. "And then they're like, 'I never saw the corona.'"

So knowing when to take the glasses off, and when to put them on, is key.

Chou says that in the last moments before the sun gets totally covered, the thin crescent of the bright sun breaks into discrete points of bright light. These are called " Baily's beads ," and they are the last bits of light from the disk of the sun shining through the valleys on the edge of the moon.

"And as they go out, their disappearance is a signal that it is now safe to remove the filters and look at the sun without a protective filter," he explains.

As soon as the sun starts to re-emerge, the glasses need to immediately go back on.

"It is possible to observe the eclipse in perfect safety," says Chou, who has seen 19 total solar eclipses.

He encourages people to go out and enjoy an event that won't happen again in the United States until 2044, even as he realizes that some people will be too fearful of eye damage.

"I recognize that there are going to be people who just don't trust the science and just don't trust the public service announcements and are just going to ignore the eclipse as much as they can," says Chou. "It's an unfortunate thing."

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