psychology definition research hypothesis

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Overview of the Scientific Method

11 Designing a Research Study

Learning objectives.

• Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
• Explain the difference between a population and a sample.
• Distinguish between experimental and non-experimental research.
• Distinguish between lab studies, field studies, and field experiments.

Identifying and Defining the Variables and Population

Variables and operational definitions.

Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A  variable  is a quantity or quality that varies across people or situations. For example, the height of the students enrolled in a university course is a variable because it varies from student to student. The chosen major of the students is also a variable as long as not everyone in the class has declared the same major. Almost everything in our world varies and as such thinking of examples of constants (things that don’t vary) is far more difficult. A rare example of a constant is the speed of light. Variables can be either quantitative or categorical. A  quantitative variable  is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable  is a quality, such as chosen major, and is typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

After the researcher generates their hypothesis and selects the variables they want to manipulate and measure, the researcher needs to find ways to actually measure the variables of interest. This requires an  operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables that researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method. Operationally defining a variable involves taking an abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. Researchers are wise to choose an operational definition that has been used extensively in the research literature.

Sampling and Measurement

In addition to identifying which variables to manipulate and measure, and operationally defining those variables, researchers need to identify the population of interest. Researchers in psychology are usually interested in drawing conclusions about some very large group of people. This is called the  population . It could be all American teenagers, children with autism, professional athletes, or even just human beings—depending on the interests and goals of the researcher. But they usually study only a small subset or  sample  of the population. For example, a researcher might measure the talkativeness of a few hundred university students with the intention of drawing conclusions about the talkativeness of men and women in general. It is important, therefore, for researchers to use a representative sample—one that is similar to the population in important respects.

One method of obtaining a sample is simple random sampling , in which every member of the population has an equal chance of being selected for the sample. For example, a pollster could start with a list of all the registered voters in a city (the population), randomly select 100 of them from the list (the sample), and ask those 100 whom they intend to vote for. Unfortunately, random sampling is difficult or impossible in most psychological research because the populations are less clearly defined than the registered voters in a city. How could a researcher give all American teenagers or all children with autism an equal chance of being selected for a sample? The most common alternative to random sampling is convenience sampling , in which the sample consists of individuals who happen to be nearby and willing to participate (such as introductory psychology students). Of course, the obvious problem with convenience sampling is that the sample might not be representative of the population and therefore it may be less appropriate to generalize the results from the sample to that population.

Experimental vs. Non-Experimental Research

The next step a researcher must take is to decide which type of approach they will use to collect the data. As you will learn in your research methods course there are many different approaches to research that can be divided in many different ways. One of the most fundamental distinctions is between experimental and non-experimental research.

Experimental Research

Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method. This is because the experimental method is the only method that allows us to determine causal relationships. Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous variables, and then they measure how the manipulated variables affect participants’ responses.

The terms independent variable and dependent variable are used in the context of experimental research. The independent variable is the variable the experimenter manipulates (it is the presumed cause) and the dependent variable is the variable the experimenter measures (it is the presumed effect).

Extraneous variables  are any variable other than the dependent variable. Confounds are a specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results. When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.

As an example, if we manipulate the lighting in the room and examine the effects of that manipulation on workers’ productivity, then the lighting conditions (bright lights vs. dim lights) would be considered the independent variable and the workers’ productivity would be considered the dependent variable. If the bright lights are noisy then that noise would be a confound since the noise would be present whenever the lights are bright and the noise would be absent when the lights are dim. If noise is varying systematically with light then we wouldn’t know if a difference in worker productivity across the two lighting conditions is due to noise or light. So confounds are bad, they disrupt our ability to make causal conclusions about the nature of the relationship between variables. However, if there is noise in the room both when the lights are on and when the lights are off then noise is merely an extraneous variable (it is a variable other than the independent or dependent variable) and we don’t worry much about extraneous variables. This is because unless a variable varies systematically with the manipulated independent variable it cannot be a competing explanation for the results.

Non-Experimental Research

Researchers who are simply interested in describing characteristics of people, describing relationships between variables, and using those relationships to make predictions can use non-experimental research. Using the non-experimental approach, the researcher simply measures variables as they naturally occur, but they do not manipulate them. For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research. Alternatively, if I stood at a busy intersection and recorded drivers’ genders and whether or not they were using a cell phone when they passed through the intersection to see whether men or women are more likely to use a cell phone when driving, then this would be non-experimental research. It is important to point out that non-experimental does not mean nonscientific. Non-experimental research is scientific in nature. It can be used to fulfill two of the three goals of science (to describe and to predict). However, unlike with experimental research, we cannot make causal conclusions using this method; we cannot say that one variable causes another variable using this method.

Laboratory vs. Field Research

The next major distinction between research methods is between laboratory and field studies. A laboratory study is a study that is conducted in the laboratory environment. In contrast, a field study is a study that is conducted in the real-world, in a natural environment.

Laboratory experiments typically have high  internal validity . Internal validity refers to the degree to which we can confidently infer a causal relationship between variables. When we conduct an experimental study in a laboratory environment we have very high internal validity because we manipulate one variable while controlling all other outside extraneous variables. When we manipulate an independent variable and observe an effect on a dependent variable and we control for everything else so that the only difference between our experimental groups or conditions is the one manipulated variable then we can be quite confident that it is the independent variable that is causing the change in the dependent variable. In contrast, because field studies are conducted in the real-world, the experimenter typically has less control over the environment and potential extraneous variables, and this decreases internal validity, making it less appropriate to arrive at causal conclusions.

But there is typically a trade-off between internal and external validity. External validity simply refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment. When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high. So laboratory studies are typically low in external validity, while field studies are typically high in external validity. Since field studies are conducted in the real-world environment it is far more appropriate to generalize the findings to that real-world environment than when the research is conducted in the more artificial sterile laboratory.

Finally, there are field studies which are non-experimental in nature because nothing is manipulated. But there are also field experiment s where an independent variable is manipulated in a natural setting and extraneous variables are controlled. Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.

A quantity or quality that varies across people or situations.

A quantity, such as height, that is typically measured by assigning a number to each individual.

A variable that represents a characteristic of an individual, such as chosen major, and is typically measured by assigning each individual's response to one of several categories (e.g., Psychology, English, Nursing, Engineering, etc.).

A definition of the variable in terms of precisely how it is to be measured.

A large group of people about whom researchers in psychology are usually interested in drawing conclusions, and from whom the sample is drawn.

A smaller portion of the population the researcher would like to study.

A common method of non-probability sampling in which the sample consists of individuals who happen to be easily available and willing to participate (such as introductory psychology students).

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

Any variable other than the dependent and independent variable.

A specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results.

A study that is conducted in the laboratory environment.

A study that is conducted in a "real world" environment outside the laboratory.

Refers to the degree to which we can confidently infer a causal relationship between variables.

Refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment.

A type of field study where an independent variable is manipulated in a natural setting and extraneous variables are controlled as much as possible.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is The Null Hypothesis & When Do You Reject The Null Hypothesis

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A null hypothesis is a statistical concept suggesting no significant difference or relationship between measured variables. It’s the default assumption unless empirical evidence proves otherwise.

The null hypothesis states no relationship exists between the two variables being studied (i.e., one variable does not affect the other).

The null hypothesis is the statement that a researcher or an investigator wants to disprove.

Testing the null hypothesis can tell you whether your results are due to the effects of manipulating ​ the dependent variable or due to random chance. 

How to Write a Null Hypothesis

Null hypotheses (H0) start as research questions that the investigator rephrases as statements indicating no effect or relationship between the independent and dependent variables.

It is a default position that your research aims to challenge or confirm.

For example, if studying the impact of exercise on weight loss, your null hypothesis might be:

There is no significant difference in weight loss between individuals who exercise daily and those who do not.

Examples of Null Hypotheses

When do we reject the null hypothesis .

We reject the null hypothesis when the data provide strong enough evidence to conclude that it is likely incorrect. This often occurs when the p-value (probability of observing the data given the null hypothesis is true) is below a predetermined significance level.

If the collected data does not meet the expectation of the null hypothesis, a researcher can conclude that the data lacks sufficient evidence to back up the null hypothesis, and thus the null hypothesis is rejected. 

Rejecting the null hypothesis means that a relationship does exist between a set of variables and the effect is statistically significant ( p > 0.05).

If the data collected from the random sample is not statistically significance , then the null hypothesis will be accepted, and the researchers can conclude that there is no relationship between the variables. 

You need to perform a statistical test on your data in order to evaluate how consistent it is with the null hypothesis. A p-value is one statistical measurement used to validate a hypothesis against observed data.

Calculating the p-value is a critical part of null-hypothesis significance testing because it quantifies how strongly the sample data contradicts the null hypothesis.

The level of statistical significance is often expressed as a  p  -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01) as general guidelines to decide if you should reject or keep the null.

When your p-value is less than or equal to your significance level, you reject the null hypothesis.

In other words, smaller p-values are taken as stronger evidence against the null hypothesis. Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis.

In this case, the sample data provides insufficient data to conclude that the effect exists in the population.

Because you can never know with complete certainty whether there is an effect in the population, your inferences about a population will sometimes be incorrect.

When you incorrectly reject the null hypothesis, it’s called a type I error. When you incorrectly fail to reject it, it’s called a type II error.

Why Do We Never Accept The Null Hypothesis?

The reason we do not say “accept the null” is because we are always assuming the null hypothesis is true and then conducting a study to see if there is evidence against it. And, even if we don’t find evidence against it, a null hypothesis is not accepted.

A lack of evidence only means that you haven’t proven that something exists. It does not prove that something doesn’t exist. 

It is risky to conclude that the null hypothesis is true merely because we did not find evidence to reject it. It is always possible that researchers elsewhere have disproved the null hypothesis, so we cannot accept it as true, but instead, we state that we failed to reject the null. 

One can either reject the null hypothesis, or fail to reject it, but can never accept it.

Why Do We Use The Null Hypothesis?

We can never prove with 100% certainty that a hypothesis is true; We can only collect evidence that supports a theory. However, testing a hypothesis can set the stage for rejecting or accepting this hypothesis within a certain confidence level.

The null hypothesis is useful because it can tell us whether the results of our study are due to random chance or the manipulation of a variable (with a certain level of confidence).

A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis.

Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists. 

Hypothesis testing is a critical part of the scientific method as it helps decide whether the results of a research study support a particular theory about a given population. Hypothesis testing is a systematic way of backing up researchers’ predictions with statistical analysis.

It helps provide sufficient statistical evidence that either favors or rejects a certain hypothesis about the population parameter. 

Purpose of a Null Hypothesis 

• The primary purpose of the null hypothesis is to disprove an assumption. 
• Whether rejected or accepted, the null hypothesis can help further progress a theory in many scientific cases.
• A null hypothesis can be used to ascertain how consistent the outcomes of multiple studies are.

Do you always need both a Null Hypothesis and an Alternative Hypothesis?

The null (H0) and alternative (Ha or H1) hypotheses are two competing claims that describe the effect of the independent variable on the dependent variable. They are mutually exclusive, which means that only one of the two hypotheses can be true. 

While the null hypothesis states that there is no effect in the population, an alternative hypothesis states that there is statistical significance between two variables. 

The goal of hypothesis testing is to make inferences about a population based on a sample. In order to undertake hypothesis testing, you must express your research hypothesis as a null and alternative hypothesis. Both hypotheses are required to cover every possible outcome of the study. 

What is the difference between a null hypothesis and an alternative hypothesis?

The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population.

It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.

What are some problems with the null hypothesis?

One major problem with the null hypothesis is that researchers typically will assume that accepting the null is a failure of the experiment. However, accepting or rejecting any hypothesis is a positive result. Even if the null is not refuted, the researchers will still learn something new.

Why can a null hypothesis not be accepted?

We can either reject or fail to reject a null hypothesis, but never accept it. If your test fails to detect an effect, this is not proof that the effect doesn’t exist. It just means that your sample did not have enough evidence to conclude that it exists.

We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.

Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.

If the p-value is greater than the significance level, then you fail to reject the null hypothesis.

Is a null hypothesis directional or non-directional?

A hypothesis test can either contain an alternative directional hypothesis or a non-directional alternative hypothesis. A directional hypothesis is one that contains the less than (“<“) or greater than (“>”) sign.

A nondirectional hypothesis contains the not equal sign (“≠”).  However, a null hypothesis is neither directional nor non-directional.

A null hypothesis is a prediction that there will be no change, relationship, or difference between two variables.

The directional hypothesis or nondirectional hypothesis would then be considered alternative hypotheses to the null hypothesis.

Gill, J. (1999). The insignificance of null hypothesis significance testing.  Political research quarterly ,  52 (3), 647-674.

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method.  American Psychologist ,  56 (1), 16.

Masson, M. E. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing.  Behavior research methods ,  43 , 679-690.

Nickerson, R. S. (2000). Null hypothesis significance testing: a review of an old and continuing controversy.  Psychological methods ,  5 (2), 241.

Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test.  Psychological bulletin ,  57 (5), 416.

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How the Experimental Method Works in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amanda Tust is a fact-checker, researcher, and writer with a Master of Science in Journalism from Northwestern University's Medill School of Journalism.

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The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.

At a Glance

While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.

What Is the Experimental Method in Psychology?

The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.

For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:

The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .

Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.

When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.

History of the Experimental Method

The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.

Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .

Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.

Other early contributors to the development and evolution of experimental psychology as we know it today include:

• Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
• Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
• Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
• Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination

Key Terms to Know

To understand how the experimental method works, it is important to know some key terms.

Dependent Variable

The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.

Independent Variable

The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.

A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.

Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.

Extraneous Variables

Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.

Demand Characteristics

Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.

Intervening Variables

Intervening variables are factors that can affect the relationship between two other variables. 

Confounding Variables

Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.

Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.

The five basic steps of the experimental process are:

• Identifying a problem to study
• Devising the research protocol
• Conducting the experiment
• Analyzing the data collected
• Sharing the findings (usually in writing or via presentation)

Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.

There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.

Lab Experiments

Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.

Field Experiments

Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.

This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.

Quasi-Experiments

While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.

A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.

So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.

Field experiments can be either quasi-experiments or true experiments.

Examples of the Experimental Method in Use

The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.

A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.

An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.

A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.

One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.

Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.

A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.

While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.

Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .

Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.

And finally, since researchers are human too, results may be degraded due to human error.

What This Means For You

Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.

At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.

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Mayrhofer R, Kuhbandner C, Lindner C. The practice of experimental psychology: An inevitably postmodern endeavor . Front Psychol . 2021;11:612805. doi:10.3389/fpsyg.2020.612805

Mandler G. A History of Modern Experimental Psychology .

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Meyer A, Hackert B, Weger U. Franz Brentano and the beginning of experimental psychology: implications for the study of psychological phenomena today . Psychol Res . 2018;82:245-254. doi:10.1007/s00426-016-0825-7

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McCambridge J, de Bruin M, Witton J.  The effects of demand characteristics on research participant behaviours in non-laboratory settings: A systematic review .  PLoS ONE . 2012;7(6):e39116. doi:10.1371/journal.pone.0039116

Laboratory experiments . In: The Sage Encyclopedia of Communication Research Methods. Allen M, ed. SAGE Publications, Inc. doi:10.4135/9781483381411.n287

Schweizer M, Braun B, Milstone A. Research methods in healthcare epidemiology and antimicrobial stewardship — quasi-experimental designs . Infect Control Hosp Epidemiol . 2016;37(10):1135-1140. doi:10.1017/ice.2016.117

Glass A, Kang M. Dividing attention in the classroom reduces exam performance . Educ Psychol . 2019;39(3):395-408. doi:10.1080/01443410.2018.1489046

Keskin M, Ooms K, Dogru AO, De Maeyer P. Exploring the cognitive load of expert and novice map users using EEG and eye tracking . ISPRS Int J Geo-Inf . 2020;9(7):429. doi:10.3390.ijgi9070429

Ho A, Hancock J, Miner A. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot . J Commun . 2018;68(4):712-733. doi:10.1093/joc/jqy026

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Torresin S, Pernigotto G, Cappelletti F, Gasparella A. Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works . Indoor Air . 2018;28(4):525-538. doi:10.1111/ina.12457

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

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7.4 Qualitative Research

Learning objectives.

• List several ways in which qualitative research differs from quantitative research in psychology.
• Describe the strengths and weaknesses of qualitative research in psychology compared with quantitative research.
• Give examples of qualitative research in psychology.

What Is Qualitative Research?

This book is primarily about quantitative research . Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of data from each of a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this is by far the most common approach to conducting empirical research in psychology, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study many psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

Again, this book is primarily about quantitative research in psychology. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This is how we know that people have a strong tendency to obey authority figures, for example, or that female college students are not substantially more talkative than male college students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this is often referred to as “thick description” (Geertz, 1973). Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this.

Data Collection and Analysis in Qualitative Research

As with correlational research, data collection approaches in qualitative research are quite varied and can involve naturalistic observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews . Interviews in qualitative research tend to be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them. The researcher can follow up by asking more detailed questions about the topics that do come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. This was essentially the approach used by Lindqvist and colleagues in their research on the families of suicide survivors. Small groups of people who participate together in interviews focused on a particular topic or issue are often referred to as focus groups . The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one-on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses.

Another approach to data collection in qualitative research is participant observation. In participant observation , researchers become active participants in the group or situation they are studying. The data they collect can include interviews (usually unstructured), their own notes based on their observations and interactions, documents, photographs, and other artifacts. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. An example of participant observation comes from a study by sociologist Amy Wilkins (published in Social Psychology Quarterly ) on a college-based religious organization that emphasized how happy its members were (Wilkins, 2008). Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

Data Analysis in Quantitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with recovering alcoholics to learn about the role of their religious faith in their recovery. Although this sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967). This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative —an interpretation—of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009). Their data were the result of unstructured interviews with 19 participants. Table 7.1 “Themes and Repeating Ideas in a Study of Postpartum Depression Among Low-Income Mothers” shows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk.…Like I really was depressed. (p. 357)

Their theoretical narrative focused on the participants’ experience of their symptoms not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances.

Table 7.1 Themes and Repeating Ideas in a Study of Postpartum Depression Among Low-Income Mothers

The Quantitative-Qualitative “Debate”

Given their differences, it may come as no surprise that quantitative and qualitative research in psychology and related fields do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). (In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches.) One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables for a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation . The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Key Takeaways

• Qualitative research is an important alternative to quantitative research in psychology. It generally involves asking broader research questions, collecting more detailed data (e.g., interviews), and using nonstatistical analyses.
• Many researchers conceptualize quantitative and qualitative research as complementary and advocate combining them. For example, qualitative research can be used to generate hypotheses and quantitative research to test them.
• Discussion: What are some ways in which a qualitative study of girls who play youth baseball would be likely to differ from a quantitative study on the same topic?

Abrams, L. S., & Curran, L. (2009). “And you’re telling me not to stress?” A grounded theory study of postpartum depression symptoms among low-income mothers. Psychology of Women Quarterly, 33 , 351–362.

Geertz, C. (1973). The interpretation of cultures . New York, NY: Basic Books.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . Chicago, IL: Aldine.

Lindqvist, P., Johansson, L., & Karlsson, U. (2008). In the aftermath of teenage suicide: A qualitative study of the psychosocial consequences for the surviving family members. BMC Psychiatry, 8 , 26. Retrieved from http://www.biomedcentral.com/1471-244X/8/26 .

Todd, Z., Nerlich, B., McKeown, S., & Clarke, D. D. (2004) Mixing methods in psychology: The integration of qualitative and quantitative methods in theory and practice . London, UK: Psychology Press.

Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Hypothesis Testing

Hypothesis testing is an important feature of science, as this is how theories are developed and modified. A good theory should generate testable predictions (hypotheses), and if research fails to support the hypotheses, then this suggests that the theory needs to be modified in some way.

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The big five factors as differential predictors of self-regulation, achievement emotions, coping and health behavior in undergraduate students

• Jesús de la Fuente 1 , 2 ,
• Paul Sander 3 ,
• Angélica Garzón Umerenkova 4 ,
• Begoña Urien 1 ,
• Mónica Pachón-Basallo 1 &
• Elkin O Luis 1  

BMC Psychology volume  12 , Article number:  267 ( 2024 ) Cite this article

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Metrics details

The aim of this research was to analyze whether the personality factors included in the Big Five model differentially predict the self-regulation and affective states of university students and health.

A total of 637 students completed validated self-report questionnaires. Using an ex post facto design, we conducted linear regression and structural prediction analyses.

The findings showed that model factors were differential predictors of both self-regulation and affective states. Self-regulation and affective states, in turn, jointly predict emotional performance while learning and even student health. These results allow us to understand, through a holistic predictive model, the differential predictive relationships of all the factors: conscientiousness and extraversion were predictors regulating positive emotionality and health; the openness to experience factor was non-regulating; nonregulating; and agreeableness and neuroticism were dysregulating, hence precursors of negative emotionality and poorer student health.

Conclusions

These results are important because they allow us to infer implications for guidance and psychological health at university.

Peer Review reports

Introduction

The personality characteristics of students have proven to be essential explanatory and predictive factors of learning behavior and performance at universities [ 1 , 2 , 3 , 4 ]. However, our knowledge about such factors does not exhaust further questions, such as which personality factors tend toward the regulation of learning behavior and which do not? Or can personality factors be arranged on a continuum to understand student differences in their emotions when learning? Consequently, the aim of this study was to analyze whether students’ personality traits differentially predict the regulation of behavior and emotionality. These variables align as different motivational-affective profiles of students, through the type of achievement emotions they experience during study, as well as their coping strategies, motivational state, and ultimately health.

Five-factor model

Previous research has shown the value and consistency of the five-factor model for analyzing students’ personality traits. Pervin, Cervone, and John [ 5 ] defined five factors as follows: (1) Conscientiousness includes a sense of duty, persistence, and behavior that is self-disciplined and goal-directed. The descriptors organized, responsible, and efficient are typically used to describe conscientious persons. (2) Extraversion is characterized by the quantity and intensity of interpersonal relationships, as well as sensation seeking. The descriptors sociable, assertive, and energetic are typically used to describe extraverted persons. (3) Openness to experience incorporates autonomous thinking and willingness to examine unfamiliar ideas and try new things. The descriptors inquisitive, philosophical, and innovative are typically used to describe persons open to experience. (4) Agreeableness is quantified along a continuum from social antagonism to compassion in one’s quality of interpersonal interactions. The descriptors inquisitive, kind, considerate, and generous are often used to describe persons characterized by agreeableness. (5) Finally, neuroticism tends to indicate negative emotions . Persons showing neuroticism are often described as moody, nervous, or touchy.

This construct has appeared to consistently predict individual differences between university students. Prior research has documented its essential role in explaining differences in achievement [ 6 , 7 ], motivational states [ 8 ], students’ learning approaches [ 9 ], self-regulated learning [ 10 ].

Five-factor model, self-regulation, achievement emotions and health

The relationship between the Big Five factors and self-regulation has been analyzed historically with much interest [ 11 , 12 , 13 , 14 , 15 ]. The dimensions of the five-factor model describe fundamental ways in which people differ from one another [ 16 , 17 ]. Of the five factors, conscientiousness may be the best reflection of self-regulation capacity. More recent research has shown consistent evidence of the relationship between these two constructs, especially conscientiousness, which has a positive relationship, and neuroticism, which has a negative relationship with self-regulation [ 18 , 19 ]. The Big Five factors are also related to coping strategies [ 20 ].

The evidence on the role of the five-factor model in self-regulation, achievement emotions, and health has been fairly consistent. On the one hand, self-regulation has a confirmed role as a meta-cognitive variable that is present in students’ mental health problems [ 21 ]. Similarly, personality factors and types of perfectionism have been associated with mental health in university students [ 22 ]. In a complementary fashion, one longitudinal study has shown that personality factors have a persistent effect on self-regulation and health. Sirois and Hirsch [ 23 ] confirmed that the Big Five traits affect balance and health behaviors.

Self-regulation, achievement emotions and health

Self-regulation has recently been considered a significant behavioral meta-ability that regulates other skills in the university environment. It has consistently appeared to be a predictor of achievement emotions [ 24 ], coping strategies [ 25 ], and health behavior [ 26 ]. In the context of university learning, the level of self-regulation is a determining factor in learning approaches, motivation and achievement [ 27 ]. Similarly, the self- vs. externally regulated behavior theory [ 27 , 28 ] assumes that the continuum of self-regulation can be divided into three types: (1) self-regulation behavior, which is the meta-behavior or meta-skill of planning and executing control over one’s behavior; (2) nonregulation behavior (deregulation) , where consistent self-regulating behavior is absent; and (3) nonregulation behavior, when regulatory behavior is maladaptive or contrary to what is expected. Some example behaviors are presented below, and these have already been documented (see Table  1 ). Recently, Beaulieu and collaborators [ 29 ] proposed a self-dysregulation latent profile for describing subjects with lower scores on subscales regarding extraversion, agreeableness and conscientiousness and higher scores concerning negative emotional facets.

Table  1 here.

Consequently, the question that we pose - as yet unresolved - is whether the different personality factors predict a determined type of regulation on the continuum of regulatory behavior, nonregulatory (deregulatory) behavior and dysregulatory behavior, based on evidence.

Aims and hypotheses

Based on the existing evidence, the aim of this study was to establish a structural predictive model that would order personality factors along a continuum as predictors of university students’ regulatory behavior. The following hypotheses were proposed for this purpose: (1) personality factors differentially predict students’ regulatory, nonregulatory and dysregulatory behavior during academic learning; they also differentially determine students’ type of emotional states (positive vs. negative affect); (2) the preceding factors differentially predict achievement emotions (positive vs. negative) during learning, coping strategies (problem-focused vs. emotion-focused) and motivational state (engagement vs. burnout); and (3) all these factors ultimately predict student health, either positively or negatively, depending on their regulatory or dysregulatory nature.

Participants

Data were gathered from 2019 to 2022, encompassing a total of 626 undergraduate students enrolled in Psychology, Primary Education, and Educational Psychology programs across two Spanish universities. Within this cohort, 85.5% were female, and 14.5% were male, with ages ranging from 19 to 24 years and a mean age of 21.33 years. The student distribution was equal between the two universities, with 324 attending one and 318 attending the other. The study employed an incidental, nonrandomized design. The guidance departments at both universities extended invitations for teacher participation, and teachers, in turn, invited their students to partake voluntarily, ensuring anonymity. Questionnaires were completed online for each academic subject, corresponding to the specific teaching-learning process.

Instruments

Five personality factors.

The Big Five Questionnaire [ 30 ], based on the version by Barbaranelli et al. [ 31 ], assessed scores for five personality factors. Confirmatory factor analysis (CFA) of the 67 scale items resulted in a five-factor structure aligned with the Big Five Model. The outcomes demonstrated satisfactory psychometric properties and acceptable fit indices. The second-order confirmatory model exhibited a good fit (chi-square = 38.273; degrees of freedom (20–15) = 5; p  > 0.10; chi/df = 7.64; RMR = 0.0425; NFI = 0.939; RFI = 0.917; IFI = 0.947; TLI = 0.937; CFI = 0.946; RMSEA = 0.065; HoeLength index = 2453 ( p  < 0.05) and 617 ( p  < 0.01)). Internal consistency of the total scale was also strong (alpha = 0.956; Part 1 = 0.932 and Part 2 = 0.832; Spearman-Brown = 0.962 and Guttman = 0.932).

Self-Regulation : The Short Self-Regulation Questionnaire (SSRQ) [ 32 ] gauged self-regulation. The Spanish adaptation, previously validated in Spanish samples [ 33 ], encompassed four factors measured by a total of 17 items. Confirmatory factor analysis confirmed a consistent factor structure (chi-square = 845.593; df = 113; chi/df = 7.483; RMSM = 0.0299; CFI = 0.959, GFI = 0.94, AGFI = 0.96, RMSEA = 0.059). Validity and reliability values (Cronbach’s alpha) were deemed acceptable (total (α = 0.86; Omega = 0.843); goal-setting planning (α = 0.79; Omega = 0.784); perseverance (α = 0.78; Omega = 0.779); decision-making (α = 0.72; Omega = 0.718); and learning from mistakes (α = 0.72; Omega = 0.722)), comparable to those of the English version. Example statements include: “I usually keep track of my progress toward my goals,” “In regard to deciding about a change, I feel overwhelmed by the choice,” and “I learn from my mistakes.”

Positive-negative affect

The Positive and Negative Affect Scale (PANAS-N) [ 34 ], validated with university students, assessed positive and negative affect. The PANAS comprises two factors and 20 items, demonstrating a consistent confirmatory factor structure (chi-square = 1111.147; df = 169; chi/df = 6.518; RMSM = 0.0346; CFI = 0.955, GFI = 0.963, AGFI = 0.96, RMSEA = 0.058). Validity and reliability values (Cronbach’s alpha) were acceptable (total (α = 0.891; Omega = 0.857); positive affect (α = 0.8199; Omega = 0.784); and negative affect (α = 0.795; Omega = 0.776), comparable to those of the English version. Sample items include “I am a lively person, I usually get excited; I have bad moods (I get upset or irritated).”

Learning Achievement Emotion : The variable was measured using the Spanish version [ 35 ] of the Achievement Emotions Questionnaire (AEQ-Learning) [ 36 ], encompassing nine emotions (enjoyment, hope, pride, relief, anger, anxiety, hopelessness, shame, and boredom). Emotions were classified based on valence (positive or negative) and activation (activating or deactivating), resulting in four quadrants. Another classification considered the source or trigger: the ongoing activity, prospective outcome, or retrospective outcome. Psychometric properties were adequate, and the confirmatory model displayed a good fit (chi-square = 529.890; degrees of freedom = 79; chi/df = 6.70; SRMR = 0.053; p  > 0.08; NFI = 0.964; RFI = 0.957; IFI = 0.973; TLI = 0.978, CFI = 0.971; RMSEA = 0.080; HOELTER = 165 ( p  < 0.05) and 178 ( p  < 0.01)). Good internal consistency was found for the total scale (Alpha = 0.939; Part 1 = 0.880, Part 2 = 0.864; Spearman-Brown = 0.913 and 884; Guttman = 0.903). Example items include Item 90: “I am angry when I have to study”; Item 113: “My sense of confidence motivates me”; and Item 144: “I am proud of myself”.

Engagement-Burnout : Engagement was assessed using a validated Spanish version of the Utrecht Work Engagement Scale for Students [ 37 ], demonstrating satisfactory psychometric properties for Spanish students. The model displayed good fit indices, with a second-order structure comprising three factors: vigor, dedication, and absorption. Scale unidimensionality and metric invariance were verified in the samples assessed (chi-square = 592.526, p  > 0.09; df = 84, chi/df = 7.05; SRMR = 0.034; TLI = 0.976, IFI = 0.954, and CFI = 0.923; RMSEA = 0.083; HOELTER = 153, p  < 0.05; 170 p  < 0.01). Cronbach’s alpha for this sample was 0.900 (14 items); the two parts of the scale produced values of 0.856 (7 items) and 0.786 (7 items).

Burnout : The Maslach Burnout Inventory (MBI) [ 38 ], in its validated Spanish version, was employed to assess burnout. This version exhibited adequate psychometric properties for Spanish students. Good fit indices were obtained, with a second-order structure comprising three factors: exhaustion or depletion, cynicism, and lack of effectiveness. Scale unidimensionality and metric invariance were confirmed in the samples assessed (chi-square = 567.885, p  > 0.010, df = 87, chi/df = 6.52; SRMR = 0.054; CFI = 0.956, IFI = 0.951, TLI = 0.951; RMSEA = 0.071; HOELTER = 224, p  < 0.05; 246 p  < 0.01). Cronbach’s alpha for this sample was 0.874 (15 items); the two parts of the scale were 0.853 (8 items) and 0.793 (7 items).

Strategies for coping with academic stress : The Coping Strategies Scale (Escala Estrategias de Coping - EEC) [ 39 ] was utilized in its original version. Constructed based on the Lazarus and Folkman questionnaire [ 40 ] using theoretical-rational criteria, the original 90-item instrument resulted in a 64-item first-order structure. The second-order structure comprised 10 factors and two significant dimensions. A satisfactory fit was observed in the second-order structure (chi-square = 478.750; degrees of freedom = 73, p  > 0.09; chi/df = 6.55; RMSR = 0.052; NFI = 0.901; RFI = 0.945; IFI = 0.903, TLI = 0.951, CFI = 0.903). Reliability was confirmed with Cronbach’s alpha values of 0.93 (complete scale), 0.93 (first half), and 0.90 (second half); Spearman-Brown coefficient of 0.84; and Guttman coefficient of 0.80. Two dimensions and 11 factors were identified: (1) Dimension: emotion-focused coping—F1. Fantasy distraction; F6. Help for action; F8. Preparing for the worst; F9. Venting and emotional isolation; F11. Resigned acceptance. (2) Dimension: problem-focused coping—F2. Help seeking and family counsel; F10. Self-instructions; F10. Positive reappraisal and firmness; F12. Communicating feelings and social support; F13. Seeking alternative reinforcement.

Student Health Behavior : The Physical and Psychosocial Health Inventory [ 41 ] measured this variable, summarizing the World Health Organization (WHO) definition of health: “Health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” The inventory focused on the impact of studies, with questions such as “I feel anxious about my studies.” Students responded on a Likert scale from 1 (strongly disagree) to 5 (strongly agree). In the Spanish sample, the model displayed good fit indices (CFI = 0.95, GFI = 0.96, NFI = 0.94; RMSEA = 0.064), with a Cronbach’s alpha of 0.82.

All participants provided informed consent before engaging in the study. The completion of scales was voluntary and conducted through an online platform. Over two academic years, students reported on five distinct teaching-learning processes, each corresponding to a different university subject they were enrolled in during this period. Students took their time to answer the questionnaires gradually throughout the academic year. The assessment for Presage variables took place in September-October of 2018 and 2019, Process variables were assessed in the subsequent February-March, and Product variables were evaluated in May-June. The procedural steps were ethically approved by the Ethics Committee under reference 2018.170, within the broader context of an R&D Project spanning 2018 to 2021.

Data analysis

The ex post facto design [ 42 ] of this cross-sectional study involved bivariate association analyses, multiple regression, and structural predictions (SEMs). Preliminary analyses were executed to ensure the appropriateness of the parameters used in the analyses, including tests for normality (Kolmogorov-Smirnov), skewness, and kurtosis (+-0.05).

Multiple regression

Hypothesis 1 was evaluated using multiple regression analysis through SPSS (v. 26).

Confirmatory factor analysis

To test Hypotheses 2 and 3, a structural equation model (SEM) was employed in this sample. Model fit was assessed by examining the chi-square to degrees of freedom ratio, along with RMSEA (root mean square error of approximation), NFI (normed fit index), CFI (comparative fit index), GFI (goodness-of-fit index), and AGFI (adjusted goodness-of-fit index) [ 43 ]. Ideally, all these values should surpass 0.90. The adequacy of the sample size was confirmed using the Hoelter index [ 44 ]. These analyses were conducted using AMOS (v.22).

Prediction results

The predictive relationships exhibited a continuum along two extremes. On the one hand, conscientiousness, extraversion and openness were significant, graded, and positive predictors of self-regulation. On the other hand, Agreeableness and Neuroticism were negative, graded predictors of self-regulation. A considerable percentage of explained variance was observed ( r 2  = 0.499). The most meaningful finding, however, is that this predictive differential grading is maintained for the rest of the variables analyzed: positive affect ( r 2  = 0.571) and negative affect ( r 2  = 0.524), achievement emotions during study, engagement burnout, problem- and emotion-focused coping strategies, and student health. See Table  2 .

Structural prediction results

Structural prediction model.

Three models were tested. Model 1 proposes the exclusive prediction of personality factors on the rest of the factors, not including self-regulation. Model 2 evaluated the predictive potential of self-regulation on the factors of the Big Five model. Model 3 tested the ability of the Big Five personality traits to predict self-regulation and the other factors. The latter model presented adequate statistical values. These models are shown in Table  3 .

Models of the linear structural results of the variables

Direct effects.

The statistical effects showed a direct, significant, positive predictive effect of the personality factors C (Conscientiousness) and E (Extraversion) on self-regulation. The result for factor O (openness to experience) was not significant. Factors A (agreeableness) and N (neuroticism) were negatively related, especially the latter. In a complementary fashion, factors C and E showed significant, positive predictions of positive affect, while O and A had less strength. Factor N most strongly predicted negative affect.

Moreover, self-regulation positively predicted positive achievement emotions during study and negatively predicted negative achievement emotions. Positive affect predicted positive emotions during study, engagement, and problem-focused coping strategies; negative affect predicted negative emotions during study, burnout, and emotion-focused strategies. Positive emotions during study negatively predict negative emotions and burnout. Engagement positively predicted problem-focused coping and negatively predicted burnout. Finally, problem-focused coping also predicted emotion-focused coping. Emotion-focused coping negatively predicts health and well-being.

Indirect effects

The Big Five factors exhibited consistent directionality. Factors C and E positively predicted positive emotions, engagement, problem-focused coping, and health and negatively predicted negative emotions and burnout. Factor O had low prediction values in both negative and positive cases. Factors A and N were positive predictors of negative emotions during study, burnout, emotion-focused coping and health, while the opposite was true for factors C and E. These factors had positive predictive effects on self-regulation, positive affect, positive emotions during study, engagement, problem-focused strategies and health; in contrast, the other factors had negative effects on negative affect, negative emotions during study, burnout, emotion-focused strategies and health. See Table  4 ; Fig.  1 .

SEM of prediction in the variables Note. C = Conscientiousness; E = Extraversion; O = Openness to experience; A = Agreeableness; N = Neuroticism; SR = Self-Regulation; Pos.A = Positive Affect; Neg.A = Negative Affect; Pe.S = Positive emotions during study; Ne.S = Negative emotions during study; ENG = Engagement; BURN = Burnout; EFCS = Emotion-focused coping strategies; PFCS = Problem-focused coping strategies: HEALTH: Health behavior.

Based on the Self- vs. External-Regulation theory [ 27 , 28 ], the aim of this study was to show, differentially, the regulatory, nonregulatory or dysregulatory power of the Big Five personality factors with respect to study behaviors, associated emotionality during study, motivational states, and ultimately, student health behavior.

Regarding Hypothesis 1 , the results showed a differential, graded prediction of the Big Five personality factors affecting both self-regulation and affective states. The results from the logistic and structural regression analyses showed a clear, graded pattern from the positive predictive relationship of C to the negative predictive relationship of N. On the one hand, they showed the regulatory effect (direct and indirect) of factors C and E, the nonregulatory effect of O, and the dysregulatory effect of factors A and especially N. This evidence offers a differential categorization of the five factors in an integrated manner. On the other hand, their effects on affective tone (direct and indirect) take the same positive direction in C and E, intermediate in the case of O, and negative in A and N. There is plentiful prior evidence that has shown this relationship, though only in part, not in the integrated manner of the model presented here [ 29 , 45 , 46 , 47 ].

Regarding Hypothesis 2 , the evidence shows that self-regulation directly and indirectly predicts affective states in achievement emotions during study. Directionality can be positive or negative according to the influence of C and E and of positive emotionality or of A and N with negative affect. This finding agrees with prior research [ 29 , 48 , 49 , 50 , 51 ].

Regarding Hypothesis 3 , the results have shown clear bidirectionality. Subsequent to the prior influence of personality factors and self-regulation, achievement emotions bring about the resulting motivational states of engagement-burnout and the use of different coping strategies (problem-focused vs. emotion-focused). Positive achievement emotions during study predicted a motivational state of engagement and problem-focused coping strategies and were positive predictors of health; however, negative emotions predicted burnout and emotion-focused coping strategies and were negative predictors of health. These results are in line with prior evidence [ 49 , 52 , 53 ]. Finally, we unequivocally showed a double, sequenced path of emotional variables and affective motivations in a process that ultimately and differentially predicts student health [ 54 , 55 ].

In conclusion, these results allow us to understand the predictive relationships involving these multiple variables in a holistic predictive model, while previous research has addressed this topic only in part [ 56 ]. We believe that these results lend empirical support to the sequence proposed by the SR vs. ER model [ 27 ]: the factors of conscientiousness and extraversion appear to be regulators of positive emotionality, engagement and health; openness to experience is considered to be nonregulating; and agreeableness and neuroticism are dysregulators of the learning process and precursors of negative emotionality and poorer student health [ 57 ]. New levels of detail—in a graded heuristic—have been added to our understanding of the relationships among the five-factor model, self-regulation, achievement emotions and health [ 23 ].

Limitations and research prospects

A primary limitation of this study was that the analysis focused exclusively on the student. The role of the teaching context, therefore, was not considered. Previous research has reported the role of the teaching process, in interaction with student characteristics, in predicting positive or negative emotionality in students [ 49 , 58 ]. However, such results do not undercut the value of the results presented here. Future research should further analyze potential personality types derived from the present categorization according to heuristic values.

Practical implications

The relationships presented may be considered a mental map that orders the constituent factors of the Five-Factor Model on a continuum, from the most adaptive (or regulatory) and deregulatory to the most maladaptive or dysregulatory. This information is very important for carrying out preventive intervention programs for students and for designing programs for those who could benefit from training in self-regulation and positivity. Such intervention could improve how students experience the difficulties inherent in university studies [ 47 , 59 ], another indicator of the need for active Psychology and Counseling Centers at universities.

Data availability

No datasets were generated or analysed during the current study.

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Fuente, J.d.l., Sander, P., Garzón Umerenkova, A. et al. The big five factors as differential predictors of self-regulation, achievement emotions, coping and health behavior in undergraduate students. BMC Psychol 12 , 267 (2024). https://doi.org/10.1186/s40359-024-01768-9

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HYPOTHESIS AND THEORY article

Artificial intelligence, human cognition, and conscious supremacy.

• 1 Sony Computer Science Laboratories, Shinagawa, Japan
• 2 Collective Intelligence Research Laboratory, The University of Tokyo, Meguro, Japan

The computational significance of consciousness is an important and potentially more tractable research theme than the hard problem of consciousness, as one could look at the correlation of consciousness and computational capacities through, e.g., algorithmic or complexity analyses. In the literature, consciousness is defined as what it is like to be an agent (i.e., a human or a bat), with phenomenal properties, such as qualia, intentionality, and self-awareness. The absence of these properties would be termed “unconscious.” The recent success of large language models (LLMs), such as ChatGPT, has raised new questions about the computational significance of human conscious processing. Although instances from biological systems would typically suggest a robust correlation between intelligence and consciousness, certain states of consciousness seem to exist without manifest existence of intelligence. On the other hand, AI systems seem to exhibit intelligence without consciousness. These instances seem to suggest possible dissociations between consciousness and intelligence in natural and artificial systems. Here, I review some salient ideas about the computational significance of human conscious processes and identify several cognitive domains potentially unique to consciousness, such as flexible attention modulation, robust handling of new contexts, choice and decision making, cognition reflecting a wide spectrum of sensory information in an integrated manner, and finally embodied cognition, which might involve unconscious processes as well. Compared to such cognitive tasks, characterized by flexible and ad hoc judgments and choices, adequately acquired knowledge and skills are typically processed unconsciously in humans, consistent with the view that computation exhibited by LLMs, which are pretrained on a large dataset, could in principle be processed without consciousness, although conversations in humans are typically done consciously, with awareness of auditory qualia as well as the semantics of what are being said. I discuss the theoretically and practically important issue of separating computations, which need to be conducted consciously from those which could be done unconsciously, in areas, such as perception, language, and driving. I propose conscious supremacy as a concept analogous to quantum supremacy, which would help identify computations possibly unique to consciousness in biologically practical time and resource limits. I explore possible mechanisms supporting the hypothetical conscious supremacy. Finally, I discuss the relevance of issues covered here for AI alignment, where computations of AI and humans need to be aligned.

1 Introduction

Recently, large language models (LLMs) have made rapid progress based on the transformer ( Vaswani et al., 2017 ) architecture, exhibiting many skills emulating but perhaps not matching human cognition, which were nonetheless once considered to be beyond the reach of machine intelligence, such as appropriate text generation based on a context, summarizing, searching under instructions, and optimization. With the advent of advanced AI systems such as ChatGPT ( Sanderson, 2023 ), questions are arising regarding the computational significance, if any, of consciousness. Despite some claims that LLMs are either already or soon becoming conscious ( Long, 2023 ), many regard these generative AI systems as doing computation unconsciously, thus forgoing possible ethical issues involved in AI abuse ( Blauth et al., 2022 ). Generic models of consciousness would also suggest the LLMs to be unconscious as a default hypothesis, unless otherwise demonstrated, e.g., by convincing behavior suggesting the presence of consciousness to an external observer or a theoretical reasoning supported by an academic consensus. If LLMs can or come close to pass human-level cognition tests such as the false belief task in the theory of mind ( Charman and Baron-Cohen, 1992 ; Baron-Cohen, 2000 ), the Turing test ( Turing, 1950 ), and Winograd schema challenge ( Sakaguchi et al., 2021 ) with their unconscious processing, what, if any, is the computational significance of consciousness?

Here, these abilities would not be necessary conditions for consciousness, as newborns are conscious without manifesting these abilities. The existence of these abilities would certainly be regarded as sufficient conditions for consciousness, in the generally accepted view of the human mind.

The theory of mind is related to the function of consciousness in the reportability and social context. The Turing test is tightly coupled with language, semantics in particular, and therefore closely related to consciousness. The Winograd schema challenge is crucial in understanding natural language, which is concerned with the nature of language here and now, locally, independent of the statistical properties dealt with in LLMs. The relation between functions exhibited by LLMs and consciousness is an interesting and timely question, especially when considering that natural language is typically processed when a human subject is conscious, except in the anecdotal and infrequent case of conversation in unconscious states, such as somniloquy ( Reimão and Lefévre, 1980 ), hypnosis ( Sarbin, 1997 ), and in a dream ( Kilroe, 2016 ), which is a state distinctive from typical conscious or unconscious states. In an apparent contradiction to the conventional assumption about the necessity of consciousness in typical natural language exchanges, computations demonstrated by LLMs are considered to be done unconsciously. If conversations involving texts partially or totally generated by LLMs virtually pass the Turing test, without computations involving consciousness, what, if any, does consciousness do computationally?

Velmans (1991) analyzed the function of consciousness in cortical information processing, taking into account the role of focus of attention, concluding that it was not clear if consciousness was necessary for cognitive processes, such as perception, learning, and creativity. Velmans elaborated on the complexity of speech production, where the tongue may make as many as 12 adjustments of shape per second, so that “within 1 min of discourse as many as 10–15 thousand neuromuscular events occur” ( Lenneberg, 1967 ). Based on these observations, Velmans suggested that speech production does not necessarily require consciousness. Such observations would necessitate a more nuanced consideration of the role of conscious and unconscious processes in language.

Apart from the conscious/unconscious divide, language occupies a central position in our understanding of consciousness. Velmans (2012) streamlined the foundations of consciousness studies, pointing out that the default position would be to reduce subjective experiences to objectively observable phenomena, such as brain function. On a more fundamental level, Velmans argued that language is associated with the dual-aspect nature of the psychophysical element of human experience, where language models the physical world only in incomplete ways, limited by the capacities of our senses. The central role of language in our understanding of the world, including consciousness, should be kept in mind when discussing artificial reproductions of language, including, but not limited to, the LLMs.

Many regard the problem of consciousness as primarily in the phenomenological domain, concerned with what is experienced by a subject when he or she is conscious, e.g., properties such as qualia, intentionality, and self-awareness as opposed to physical or functional descriptions of the brain function. There are experimental and theoretical approaches tackling the cognitive implications of consciousness based on ideas, such as neural correlates of consciousness (NCC, Crick and Koch, 1998 ; Koch et al., 2016 ), global workspace theory ( Baars, 1997 , 2005 ), integrated information theory ( Tononi et al., 2016 ), and free-energy principle ( Friston, 2010 ).

Wiese and Friston (2021) discussed the relevance of the free-energy principle as a constraint for the computational correlates of consciousness (CCC), stressing the importance of neural dynamics, not states. In their framework, trajectories rather than states are mapped to conscious experiences. They propose CCC as a more general concept than the neural correlates of consciousness (NCC), discussing the nature of the correlates as necessary, sufficient, or both conditions for consciousness.

Some, somewhat controversially, consider quantum effects as essential in explaining the nature of consciousness ( Hameroff, 1998 ; Woolf and Hameroff, 2001 ). Although there have been significant advances made, explaining the hard problem of consciousness ( Chalmers, 1995 ) from such theoretical approaches remains hypothetical at best, even if not cognitively closed ( McGinn, 1994 ), and a scientific consensus has not been reached yet. There are also arguments that hold that the hard problem is not necessarily essential for the study of consciousness. Seth (2021) argued that if we pursue the real problem of accounting for properties of consciousness in terms of biological mechanisms, the hard problem will turn out to be less important.

Given the difficulty in studying the phenomenological aspects of consciousness, with the advancement in artificial intelligence (AI), there is now a unique opportunity to study the nature of consciousness by approaching it from its computational significance. As artificial intelligence systems, such as LLMs, are reproducing and even surpassing human information processing capabilities, the identification of computational elements possibly unique to consciousness is coming under more focused analysis.

At present, it is difficult to give a precise definition of what computations unique to consciousness are. What follows are tentative descriptions adopted in this paper. From the objective point of view, neural computation correlating with consciousness would typically involve large areas of the brain processing information in coherent and integrated parallel manners, while sensory qualia represent the result of complex processing in compressed forms, as in color constancy ( Foster, 2011 ). Unconscious computation, on the other hand, does not meet these criteria. From the subjective point of view, conscious computation would be accompanied by such properties as qualia, intentionality, and self-consciousness. Unconscious computations do not cause these aspects of experience to emerge.

Artificial intelligence is an umbrella term, and its specific capabilities depend on parameters and configurations of system makeup and dynamics. For now, we would assume that AI systems referred to here are realized on classical computers. AI systems constructed on quantum computers might exhibit broader ranges of computational capabilities, possibly exhibiting quantum supremacy ( Arute et al., 2019 ), which describes the abilities of quantum computers to solve problems any classical computer could not solve in any practical time. Quantum supremacy is not a claim that quantum computers would be able to execute computations beyond what universal Turing machines ( Turing, 1936 ) are capable of. It is rather a claim that quantum computers can, under the circumstances, execute computations that could, in principle, be done by classical computers, but not within any practical period considering the physical time typically available to humans.

Similarly, conscious supremacy can be defined as domains of computation that can be conducted by conscious processes but cannot be executed by systems lacking consciousness in any practical time. Since the science of consciousness has not yet developed to reach the same level as quantum mechanics, it is difficult to give a precise definition of what conscious supremacy is at present. What follows is a tentative definition adopted in this article. Out of all the computations done in the neural networks in the brain, conscious supremacy refers to those areas of computation accompanied by consciousness, which are done in efficient and integrated ways compared to unconscious computation. Given the limits of resources available in the brain, computations executed in conscious supremacy would be, in a practical sense, impossible to execute by unconscious computation in any meaningful biological time. However, in principle, they could be done. Thus, there are no distinctions between computations belonging to conscious supremacy and other domains in terms of computability in principle. The practical impossibility of non-conscious systems to execute computations belonging to conscious supremacy would have been one of the adaptive values of consciousness in evolution.

The relationship between quantum supremacy and conscious supremacy will be discussed later.

As of now, quantum supremacy remains controversial ( McCormick, 2022 ). The merit of introducing the perhaps equally debatable concept of conscious supremacy is that we can hope to streamline aspects of computation conducted by conscious and unconscious processes.

Abilities to play board games, such as chess, shogi, and go, are no longer considered to be unique to human cognition after AI systems, such as Deep Blue ( Campbell et al., 2002 ) and AlphaZero ( Schrittwieser et al., 2020 ), defeated human champions. After the success of LLMs in executing a large part of natural language tasks, cognitive abilities once considered unique to humans, e.g., the theory of mind, Turing test, and Winograd schema challenge, might not be considered to be verifications of the ability of artificial intelligence systems to perform cognitive tasks on par with humans. It should be noted that the attribution of the theory of mind to LLMs remains controversial ( Aru et al., 2023 ), and the exact nature of cognitive functions related to natural language, if any, in LLMs is an open question. However, it does seem legitimate to start considering the exclusion of certain computations from the set of those unique to consciousness based on computational evidence. While such exclusion might reflect cognitive biases on the part of humans to raise the bar unfavorably for AI systems, in an effort to solve cognitive dissonance ( Aronson, 1969 ) about the relative superiorities of AI and humans, such considerations could serve as a filter to fine-tune domains of cognitive tasks uniquely executed by human cognition, conscious, and unconscious.

As artificial intelligence systems based on deep learning and other approaches advance in their abilities, tasks considered to be uniquely human would gradually diminish in the spectrum of functionalities. Specifically, the set X of computations considered unique to humans would be the complement of the union of the set of computations executed by artificial intelligence systems A 1 , A 2 , …, A N under consideration. Namely, X = A c , where A = A 1 UA 2 U… UA N ( Figure 1 ), where the whole set represents the space of possible computations conducted by humans. As the number of artificial intelligence systems increases, the uniquely human domain of computation would ultimately become X ∞  = A ∞ c , where A ∞  = lim N- > ∞ A 1 UA 2 U… UA N .

Figure 1 . The analysis of AI capabilities would help focus the computational domain unique to consciousness (X), which can be defined in terms of instances of AI systems. As the number of AI systems increases, computations unique to consciousness will be more finely defined.

Needless to say, such an argument is conceptual in nature, as it is difficult to draw a clear line between what could and could not be done by artificial intelligence systems at present. Among computations unique to humans, some would be executed consciously, while some might be a combination of conscious and unconscious computation, involving processes which lie either inside or outside the neural correlates of consciousness ( Crick and Koch, 1998 ; Koch et al., 2016 ). Theoretically, there could also be computations unique to humans executed unconsciously, although not of central interest in the context adopted here.

Penrose suggested that consciousness is correlated with the quantum mechanical effect, possibly involving quantum gravity ( Penrose, 1996 ). Penrose went on to collaborate with Stuart Hameroff. Penrose and Hameroff together suggested, in a series of papers ( Hameroff and Penrose, 1996 ; Hameroff and Penrose, 2014 ), that quantum mechanical processes in microtubules were involved in conscious processes, which went beyond the algorithmic capabilities of computability for the classical computer. Specifically, it was postulated that a process named “Orchestrated objective reduction” (Orch OR) was responsible for the generation of proto-consciousness in microtubules, a hypothesis independent from conventional arguments on quantum computing. One of the criticisms directed to such quantum models of consciousness was based on the fact that temperatures in biological systems are typically too high for quantum coherence or entanglement to be effective ( Tegmark, 2000 ).

2 Possibilities and limits of artificial intelligence systems

Artificial General Intelligence (AGI; Goertzel, 2014 ) is purported to execute all tasks carried out by a typical human brain and beyond. Proposed tasks to be executed by AGI include the Turing test, coffee making or Wozniak test ( Adams et al., 2012 ), college enrollment test ( Goertzel, 2014 ), employment test ( Scott et al., 2022 ), and the discovery of new scientific knowledge ( Kitano, 2016 ).

In identifying possible areas for uniquely human cognition and potential candidates for conscious supremacy, it is useful to discuss systemic potentials and limits of artificial intelligence, which are currently apparent.

Some LLMs have started to show sparks of general intelligence ( Bubeck et al., 2023 ) beyond abilities for linguistic processing. Such a potential might be explained by the inherent functions of language. The lexical hypothesis ( Crowne, 2007 ) states that important concepts in fields, such as personality study and general philosophy, would be expressible by everyday language. The ability of natural language to represent and analyze a wide range of information in the environment is consistent with the perceived general ability of LLMs to represent various truths about this world, without necessarily being conscious, thus suggesting the central importance of representation in the analysis of intelligence.

What is meant by representation is a potentially controversial issue. In the conventional sense of psychology and philosophy of mind, a representation refers to the internal state that corresponds to an external reality ( Marr, 1982 ). In the constructivist approach, representation would be an active construct of an agent’s knowledge, not necessarily requiring an external reality as a prior ( Von Glasersfeld, 1987 ). Representations in artificial intelligence systems would be somewhere in between, taking inspiration from various lines of theoretical approaches.

One of the problems with LLMs, such as ChatGPT, is the occurrence of hallucination ( Ji et al., 2023 ) and the tendency to produce sentences inconsistent with accepted facts, a term criticized by some researchers as an instance of anthropomorphism. Although humans also suffer from similar misconceptions, subjects typically are able to make confident judgments about their own statements ( Yeung and Summerfield, 2012 ), while methods for establishing similar capabilities in artificial intelligence systems have not been established. Regarding consciousness, metacognitive processes associated with consciousness ( Nelson, 1996 ) might help rectify potential errors in human cognition.

Behaviorist ways of thinking ( Araiba, 2019 ) suggest that human thoughts are ultimately represented in terms of bodily movements. No matter how well developed an intelligent agent might be, manifestations of its functionality would ultimately be found in its objective courses of action in the physical space. From this perspective, the intelligence of an agent would be judged in terms of its external behavior, an idea in AI research sometimes called instrumental convergence ( Bostrom, 2012 ).

The possibilities and limits of artificial intelligence systems would be tangibly assessed through analysis of behavior. In voluntary movement, evidence suggests that consciousness is involved in vetoing a particular action (free won’t) when it is judged to be inappropriate within a particular context ( Libet, 1999 ).

Thus, from robust handling of linguistic information to streamlining of external behavior, metacognitive monitoring and control would be central in identifying and rectifying limits of artificial intelligence systems, a view consistent with the idea that metacognition plays an essential role in consciousness ( Nelson, 1996 ).

3 Computations possibly unique to conscious processing

As of now, the eventual range of computational capabilities of artificial intelligence is unclear. Employing cognitive arguments based on the observation of what subset of computation is typically done consciously, in addition to insights on the limits of artificial intelligence, would help narrow down possible consciousness-specific tasks. In that process, the division of labor between conscious and unconscious processes could be made, as we thus outline heterogeneous aspects of cognition.

Acquiring new skills or making decisions in novel contexts would typically require the involvement of conscious processing, while the execution of acquired skills would proceed largely unconsciously ( Solomon, 1911 ; Lisman and Sternberg, 2013 ) in terms of the accompanying phenomenological properties, such as qualia, intentionality, and attention. Any cognitive task, when it needs to integrate information analyzed across many different regions in the brain, typically requires consciousness, reflecting the global nature of consciousness in terms of cortical regions involved ( Baars, 2005 ). The autonomous execution of familiar tasks would involve a different set of neural networks compared to the minimum set of neural activities (neural correlates, Koch et al., 2016 ) required for the sustaining of consciousness.

It is interesting to note here that some self-learning unsupervised artificial intelligence systems seem to possess abilities to acquire new skills and make decisions in novel contexts ( Silver et al., 2017 ; Schrittwieser et al., 2020 ). As the ability of artificial intelligence systems approaches the level purported for AGI ( Goertzel, 2014 ), the possibility of the emergence of consciousness might have to be considered.

The global neural workspace (GNW) theory ( Dehaene et al., 1998 ; Mashour et al., 2020 ) addresses how the neural networks in the brain support a dynamic network where relevant information can be assessed by local networks, eventually giving rise to consciousness. The multimodal nature of the GNW theory has inspired various theoretical works, including those related to deep learning networks ( LeCun et al., 2015 ; Bengio, 2017 ).

In evolution, one of the advantages of information processing involving consciousness might have been decision-making reflecting a multitude of sensory inputs. Multimodal perception typically subserves such a decision-making process. Since the science of decision-making is an integral part of AI alignment ( Yudkowsky, 2015 ), the difference between conscious and unconscious, as well as human and AI decision-making processes, would shed much light on the parameters of systems supporting the nature of conscious computation.

Technological issues surrounding self-driving cars ( Badue et al., 2021 ) have emerged as one of the most important research themes today, both from theoretical and practical standpoints. Driving cars involves a series of judgments, choices, and actions based on multimodal sensory information. Judgments on how to drive a vehicle often must be done within limited time windows in ad hoc situations, affected by the unpredictability of other human drivers, if any, and there are still challenges toward realizing fully self-driving vehicles ( Kosuru and Venkitaraman, 2023 ). Moral dilemmas involved in driving judgments require sorting out situations concerned with conflicting choices for safety, known collectively as the trolley problem ( Thomson, 1985 ), which is often intractable even when presented with clear alternative schemes ( Awad et al., 2018 ). In real-life situations, there would be perceptual and cognitive ambiguities about, for example, whether you can really save five people by sacrificing one. In the face of such difficulties, fully self-driving cars without conscious human interventions might turn out to be impossible ( Shladover, 2016 ).

The language is a series of micro-decisions, in that words must be selected, depending on the context, as follow-up sequences on what has been already expressed. The apparent success of LLMs in reproducing salient features of embedded knowledge in the language ( Singhal et al., 2023 ) is impressive. However, it might still fall short of executing situated or embodied choice of words, as required, for example, in the college enrollment and employment tests. A linguistic generative AI might nominally pass the Turing test in artificial and limited situations. However, when an AI system implemented in a robot interacts with a human in real-life situations, there might be a perceived uncanny valley ( Mori, 2012 ) linguistically, where negative emotions, such as uneasiness and repulsion, might be hypothetically induced in a human subject as the performance comes nearer to the human level.

4 Possible mechanisms for conscious supremacy

It is possible that there are computations uniquely executed by conscious processes, and there could be some similarities between conscious and quantum computations, independent of whether consciousness actually involves quantum processes in the brain. There could be similarities between postulated quantum supremacy and conscious supremacy, without underlying common mechanisms being necessarily implicated. It is worth noting here that just as it is in principle possible to simulate quantum computing on classical computers, it might be possible to simulate conscious computing, regardless of its nature, on classical computers, e.g., in terms of connectionist models representing neural networks in the brain.

There are several algorithms that demonstrate the superiority of quantum computing. For example, Schor’s algorithm ( Shor, 1994 ) can find prime factors of large numbers efficiently. Given a large number N, Shor’s algorithm for finding prime factors can run in polynomial time in terms of N, compared to sub-exponential time on optimal algorithms for a classical computer.

In conscious visual perception, the binding problem ( Feldman, 2012 ) questions how the brain integrates visual features, such as colors and forms, into coherent conscious percepts. The challenge of combinatorial explosion ( Treisman, 1999 ), in which all possible combinations of features, such as the yellow (color) Volkswagen Beetle car (form), must be dealt with, becomes essential there. Given the fact that forms ( Logothetis et al., 1995 ) and colors ( Zeki and Marini, 1998 ) are represented by distributed circuits in the brain, sorting through the possible combinations of forms and colors has similarities with the factoring problem addressed by Shor’s algorithm ( Figure 2 ).

Figure 2 . Analogy between finding prime factors and integration of visual features. (A) Finding prime factors for a large number becomes increasingly difficult for classical computers. Quantum computing employing Shor’s algorithm provides an efficient method for factoring large natural numbers. (B) Sorting out combinatorial explosion in the integration of visual features represented in distributed neural networks in the brain is a still unresolved challenge known as the binding problem. The picture was generated by Dall-E (Open AI) with the prompt: A yellow Volkswagen Beetle car surrounded by cars of different shapes and colors seen from a distance in manga style.

In quantum computing ( Deutsch, 1985 ; Feynman, 1985 ), quantum superposition and entanglement are ingeniously employed to conduct algorithms effectively impossible for classical computers to execute in realistic time frames. In a quantum computing process, decoherence would introduce noise, and in order to execute on a large scale, a process called quantum error correction (QEC; Cai and Ma, 2021 ) is essential.

In conscious computing discussed here, similar mechanisms might be at play. For example, the contrast between the noisy neural firings and the apparently Platonic phenomenology of qualia suggests a process in which the variabilities due to noise in neural firings are rectified, named here conscious error correction (CEC). At present, the plausibility or the details of such an error-rectifying scheme is not clear. The possible relationships (if any) between QEC and CEC remain speculative at best at the moment. Despite these reservations, the involvement of error-correcting mechanisms in consciously conducted computation would be a line of thought worth investigating.

5 Implications for AI alignment

As artificial intelligence systems make progress, it is becoming important to align them with humans, an area called AI alignment ( Russell and Norvig, 2021 ).

The elucidation of computations uniquely executed by consciousness and the possible existence of conscious supremacy, i.e., computations specifically and uniquely executed by neural processes correlating with consciousness, would put a constraint on AI alignment schemes.

Specifically, it would be an efficient alignment strategy to develop AI systems with capabilities other than uniquely conscious computations, while leaving computation involving conscious supremacy to humans.

It is interesting to consider the implications of such divisions of labor between AIs and humans for AI safety ( Zhang et al., 2021 ). It would be impractical to require AI systems to carry out tasks better left to humans. Expecting AIs to execute tasks belonging to conscious supremacy would significantly disrupt AI safety.

Eliezer Yudkowsky’s conceptualization of Friendly AI ( Yudkowsky, 2008 ) is based on the importance of updating the system in accordance with humans ( Russell and Norvig, 2021 ). Reinforcement learning from human feedback (RLHF; Stiennon et al., 2020 ), a technique often used in the development of artificial intelligence systems, can be considered to be an instance of developing Friendly AI and an attempt at the division of labor between conscious (human) and unconscious (AI) computations.

Alignment of AIs with humans, in the context of AI safety in particular, would depend on an effective division of labor between cognition unique to humans centered on conscious supremacy and computation conducted by computers, in a way similar to the interaction between conscious and unconscious processes in the human brain. In this context, artificial intelligence systems can be regarded as extensions of unconscious processes in the brain. Insights on cortical plasticities from tool use ( Iriki et al., 1996 ) could provide relevant frameworks for discussion. It is important to note that limiting the functions of artificial intelligence systems to non-conscious operations does not necessarily guarantee robust alignment. Alignment would also depend on parameters that are dependent on the developers and stakeholders in the ecosystem of artificial intelligence. It would be important to discuss various aspects concerning alignment, including those put forward here.

Finally, the development of artificial consciousness ( Chrisley, 2008 ), whether theoretically or practically feasible or not, might not be an effective strategy for AI alignment. From the point of view of the division of labor, computational domains belonging to conscious supremacy would be better left to humans. Artificial intelligence systems would do a better job of alignment by trying to augment computations unique to consciousness, which are to be reasonably executed by humans, rather than by replacing them from scratch.

6 Discussion

I have addressed here the possibility of characterizing conscious processes from a computational point of view. The development of artificial intelligence systems provides unique opportunities to explore and focus more deeply on computational processes unique to consciousness.

At present, it is not clear whether consciousness would eventually emerge from present lines of research and development in artificial intelligence. It would be useful to start from the null hypothesis of the non-existence of consciousness in artificial intelligence systems. We would then be able to narrow down what consciousness uniquely computes.

I have proposed the concept of conscious supremacy. Although this is speculative at present, it would be useful to think in terms of computational contexts apart from the hard problem of the phenomenology of consciousness. The presence of conscious supremacy would be connected to the advantages the emergence of consciousness has provided in the history of evolution. Elucidating the nature of conscious supremacy would help decipher elements involved in consciousness, whether it is ultimately coupled with quantum processes or not.

The value of arguments presented in this paper is limited, as it has not yet specifically identified computations unique to consciousness. The efforts to characterize computations unique to consciousness in terms of conscious supremacy presented here would hopefully help streamline discussions on this issue, although, needless to say, much work remains to be done.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

KM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

The author declares that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

Author KM was employed by Sony Computer Science Laboratories.

Publisher’s note

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Keywords: conscious supremacy, artificial intelligence, consciousness, large language model, computation

Citation: Mogi K (2024) Artificial intelligence, human cognition, and conscious supremacy. Front. Psychol . 15:1364714. doi: 10.3389/fpsyg.2024.1364714

Received: 02 January 2024; Accepted: 26 April 2024; Published: 13 May 2024.

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*Correspondence: Ken Mogi, [email protected]

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