This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

hypothesis

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

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  • How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

  • An independent variable is something the researcher changes or controls.
  • A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism. Run a free check.

Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

  • The relevant variables
  • The specific group being studied
  • The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

  • H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
  • H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

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hypothesis about theory

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

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

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

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Hypothesis vs. Theory

A hypothesis is either a suggested explanation for an observable phenomenon, or a reasoned prediction of a possible causal correlation among multiple phenomena. In science , a theory is a tested, well-substantiated, unifying explanation for a set of verified, proven factors. A theory is always backed by evidence; a hypothesis is only a suggested possible outcome, and is testable and falsifiable.

Comparison chart

Examples of theory and hypothesis.

Theory: Einstein's theory of relativity is a theory because it has been tested and verified innumerable times, with results consistently verifying Einstein's conclusion. However, simply because Einstein's conclusion has become a theory does not mean testing of this theory has stopped; all science is ongoing. See also the Big Bang theory , germ theory , and climate change .

Hypothesis: One might think that a prisoner who learns a work skill while in prison will be less likely to commit a crime when released. This is a hypothesis, an "educated guess." The scientific method can be used to test this hypothesis, to either prove it is false or prove that it warrants further study. (Note: Simply because a hypothesis is not found to be false does not mean it is true all or even most of the time. If it is consistently true after considerable time and research, it may be on its way to becoming a theory.)

This video further explains the difference between a theory and a hypothesis:

Common Misconception

People often tend to say "theory" when what they're actually talking about is a hypothesis. For instance, "Migraines are caused by drinking coffee after 2 p.m. — well, it's just a theory, not a rule."

This is actually a logically reasoned proposal based on an observation — say 2 instances of drinking coffee after 2 p.m. caused a migraine — but even if this were true, the migraine could have actually been caused by some other factors.

Because this observation is merely a reasoned possibility, it is testable and can be falsified — which makes it a hypothesis, not a theory.

  • What is a Scientific Hypothesis? - LiveScience
  • Wikipedia:Scientific theory

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Comments: Hypothesis vs Theory

Anonymous comments (2).

October 11, 2013, 1:11pm "In science, a theory is a well-substantiated, unifying explanation for a set of verified, proven hypotheses." But there's no such thing as "proven hypotheses". Hypotheses can be tested/falsified, they can't be "proven". That's just not how science works. Logical deductions based on axioms can be proven, but not scientific hypotheses. On top of that I find it somewhat strange to claim that a theory doesn't have to be testable, if it's built up from hypotheses, which DO have to be testable... — 80.✗.✗.139
May 6, 2014, 11:45pm "Evolution is a theory, not a fact, regarding the origin of living things." this statement is poorly formed because it implies that a thing is a theory until it gets proven and then it is somehow promoted to fact. this is just a misunderstanding of what the words mean, and of how science progresses generally. to say that a theory is inherently dubious because "it isn't a fact" is pretty much a meaningless statement. no expression which qualified as a mere fact could do a very good job of explaining the complicated process by which species have arisen on Earth over the last billion years. in fact, if you claimed that you could come up with such a single fact, now THAT would be dubious! everything we observe in nature supports the theory of evolution, and nothing we observe contradicts it. when you can say this about a theory, it's a pretty fair bet that the theory is correct. — 71.✗.✗.151
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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

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Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

  • Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
  • Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
  • Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
  • Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

What Is a Hypothesis? (Science)

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A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

  • Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

  • If you drop a rock and a feather, (then) they will fall at the same rate.
  • Plants need sunlight in order to live. (if sunlight, then life)
  • Eating sugar gives you energy. (if sugar, then energy)
  • White, Jay D.  Research in Public Administration . Conn., 1998.
  • Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
  • Null Hypothesis Definition and Examples
  • Definition of a Hypothesis
  • What Are the Elements of a Good Hypothesis?
  • Six Steps of the Scientific Method
  • What Are Examples of a Hypothesis?
  • Understanding Simple vs Controlled Experiments
  • Scientific Method Flow Chart
  • Scientific Method Vocabulary Terms
  • What Is a Testable Hypothesis?
  • Null Hypothesis Examples
  • What 'Fail to Reject' Means in a Hypothesis Test
  • How To Design a Science Fair Experiment
  • What Is an Experiment? Definition and Design
  • Hypothesis Test for the Difference of Two Population Proportions
  • How to Conduct a Hypothesis Test

Christopher Dwyer Ph.D.

What Is a "Theory" and Why Is It Important to Know?

Critically thinking about epistemology and the notion of "theory.".

Posted July 31, 2020 | Reviewed by Ekua Hagan

When asked the question, "What is a theory?" most of my students (regardless of age or educational level) respond with an explanation that is akin to "a reasonable, educated/informed guess."

Indeed, consistent with this perspective, the phrase "it’s just a theory" (be it in reference to one’s humility regarding their own standpoint or trying to denigrate another) is one I’m sure many of you have heard thrown around from time to time. The problem is that these common perspectives are not correct—which makes me wonder, is the majority of the population misinformed as to what theory refers to?

While many conceptualise a theory as a reasonable, educated guess, what they’re really describing is a hypothesis (i.e. a proposed outcome, explained on the basis of limited evidence or a thread of logic as a starting point for further investigation). A theory is more concrete than an educated guess. In order to appropriately explain the concept of theory, it’s important to first set the scene:

For centuries, it was believed that all swans are white. Then one day, a black swan (cygnus atratus) was spotted and "knowledge" had to be amended. The original perspective was falsified (e.g. see Popper, 1934/1959). More recently, knowledge once again required amendment—there are no longer nine planets, but rather eight. The more knowledge we obtain, the better our understanding becomes; and as we come to understand more, further amendments may be required to what we once thought we knew. Critical thought is cautious and accounts for amendment when necessary (e.g. see discussion on reflective judgmen t and "proof ").

Let’s consider another example. We are familiar with the "Law of Gravity"—a crude description of its function on Earth being the acceleration of objects to the ground at 9.81 m/s 2 . However, calling gravity a "law" is a misnomer. Gravity, as I imagine many readers will know, is actually a theory. But why?

Let’s be clear, if I’m holding a coffee bean in my hand and release it, I’m going to bet my house that the outcome will be that it falls to the ground. However, we can never be 100% certain that this event will occur; and there are numerous reasons for this. Let’s discuss two here.

First, what if our understanding of gravity is incorrect? What if there are, as of yet, unobserved characteristics of our current conceptualisation of gravity? What if there’s more to it than we think? You might say, "Surely, we would have seen such characteristics by now?" Well, the same could be said about being able to count planets. Remember, neither of these examples/potentialities is a function of being wrong about a phenomenon, rather they are a function of learning more.

Second, we cannot see into the future—we can never be entirely certain that something will happen; though we might have a strong theory as to what will happen (e.g. the coffee bean will likely fall to the ground). Our hypothesis is one of extreme confidence . Why? Well, gravity is a strong theory. But what happens if an asteroid hits the Earth tomorrow, knocks us off-axis, changes our polarity, and plays games with our planet’s electromagnetism? Perhaps "gravity" will then behave differently. Of course, this is extremely unlikely; but, there is still the possibility, no matter how minute; and as a chance exists (regardless of how minute), that means that we cannot be 100% certain of the original premise.

Again, this talk of gravity is a rather extreme analogy for my point; and in no way, shape or form do I question or will I test the force of gravity, but it does provide a good example for consideration. So, then, is a theory a law? No, simply, this example makes the point that the use of "laws," in this context, is inaccurate. So, what actually is a theory?

A theory is an established model for why or how a given phenomenon occurs—it is an explanation of observed regularities. The terms "established" and "observed regularities" are important here. Theories are developed based on observing similar outcomes over and over again. This is a fundamental reason why replication in research is so important. It is also why any one piece or even bodies of research cannot "prove" a theory true; rather replication provides further evidence to support a theory—it strengthens a theory. Returning to the example of gravity, it is such a strong theory because it has been observed time and time again without ever being falsified.

Okay, so a theory is a much stronger notion than many may have thought. It’s certainly stronger than an educated guess. But, how does that affect your day-to-day life? Well, knowing what a theory actually is will help your decision-making in terms of navigating the terrain of what research says in relation to how society and media represent it. For example, when someone you know states that "evolution is just a theory," you know that it actually means that the concept of evolution is based on a model of replicated data observed time and time again; thus making it a leading explanation for why events in that particular context occur—it’s not just some unestablished guess.

hypothesis about theory

Likewise, you may have in the past said to yourself or explained to your friend that some notion is "just my theory" – unless the phenomenon has been observed regularly, over and over, you know that such a perspective is inaccurate. Notably, if it’s something that you, alone, have observed time and time again, it’s still inaccurate—your personal experience and anecdotal evidence are not sufficient grounds to develop a theory. The observation requires replication by others as well. Truly understanding what a theory is and the mechanics behind falsification are fantastic ways for individuals to begin embracing the concept of intellectual humility, through engaging in epistemological consideration regarding the nature of knowledge and the concept of "certainty."

In conclusion, a theory is much more than a hypothesis—it comes from a strong evidence base and should not be cast aside as if it were a guess. If you truly care about the topic you are thinking about, you will consider empirically-based theories. However, just because you know that a theory is an established model for why or how a given phenomenon occurs, doesn’t mean that everyone else does. Be cautious in interpreting how people throw the term around and strive for clarification.

Popper, K.R. (1934/1959). The logic of scientific discovery. London: Routledge.

Christopher Dwyer Ph.D.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

A girl looks at plants in a test tube for a science experiment. What's her scientific hypothesis?

Hypothesis basics

What makes a hypothesis testable.

  • Types of hypotheses
  • Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

  • If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
  • If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
  • If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

  • Read more about writing a hypothesis, from the American Medical Writers Association.
  • Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
  • Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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1.2: Theories, Hypotheses and Models

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For the purpose of this textbook (and science in general), we introduce a distinction in what we mean by “theory”, “hypothesis”, and by “model”. We will consider a “theory” to be a set of statements (or an equation) that gives us a broad description, applicable to several phenomena and that allows us to make verifiable predictions. For example, Chloë’s Theory ( \(t \propto \sqrt{h}\) ) can be considered a theory. Specifically, we do not use the word theory in the context of “I have a theory about this...”

A “hypothesis” is a consequence of the theory that one can test. From Chloë’s Theory, we have the hypothesis that an object will take \(\sqrt{2}\) times longer to fall from \(1\:\text{m}\) than from \(2\:\text{m}\) . We can formulate the hypothesis based on the theory and then test that hypothesis. If the hypothesis is found to be invalidated by experiment, then either the theory is incorrect, or the hypothesis is not consistent with the theory.

A “model” is a situation-specific description of a phenomenon based on a theory , that allows us to make a specific prediction. Using the example from the previous section, our theory would be that the fall time of an object is proportional to the square root of the drop height, and a model would be applying that theory to describe a tennis ball falling by \(4.2\) m. From the model, we can form a testable hypothesis of how long it will take the tennis ball to fall that distance. It is important to note that a model will almost always be an approximation of the theory applied to describe a particular phenomenon. For example, if Chloë’s Theory is only valid in vacuum, and we use it to model the time that it take for an object to fall at the surface of the Earth, we may find that our model disagrees with experiment. We would not necessarily conclude that the theory is invalidated, if our model did not adequately apply the theory to describe the phenomenon (e.g. by forgetting to include the effect of air drag).

This textbook will introduce the theories from Classical Physics, which were mostly established and tested between the seventeenth and nineteenth centuries. We will take it as given that readers of this textbook are not likely to perform experiments that challenge those well-established theories. The main challenge will be, given a theory, to define a model that describes a particular situation, and then to test that model. This introductory physics course is thus focused on thinking of “doing physics” as the task of correctly modeling a situation.

Emma's Thoughts

What’s the difference between a model and a theory?

“Model” and “Theory” are sometimes used interchangeably among scientists. In physics, it is particularly important to distinguish between these two terms. A model provides an immediate understanding of something based on a theory.

For example, if you would like to model the launch of your toy rocket into space, you might run a computer simulation of the launch based on various theories of propulsion that you have learned. In this case, the model is the computer simulation, which describes what will happen to the rocket. This model depends on various theories that have been extensively tested such as Newton’s Laws of motion, Fluid dynamics, etc.

  • “Model”: Your homemade rocket computer simulation
  • “Theory”: Newton’s Laws of motion, Fluid dynamics

With this analogy, we can quickly see that the “model” and “theory” are not interchangeable. If they were, we would be saying that all of Newton’s Laws of Motion depend on the success of your piddly toy rocket computer simulation!

Exercise \(\PageIndex{2}\)

Models cannot be scientifically tested, only theories can be tested.

“Theory” vs. “Hypothesis”: What Is The Difference?

Chances are you’ve heard of the TV show The Big Bang Theory . Lots of people love this lighthearted sitcom for its quirky characters and their relationships, but others haven’t even given the series a chance for one reason: they don’t like science and assume the show is boring.

However, it only takes a few seconds with Sheldon and Penny to disprove this assumption and realize that this theory ab0ut The Big Bang Theory is wrong—it isn’t a scientific snoozefest.

But wait: is it a theory or a  hypothesis about the show that leads people astray? And would the actual big bang theory— the one that refers to the beginning of the universe—mean the same thing as a big bang hypothesis ?

Let’s take a closer look at theory and hypothesis to nail down what they mean.

What does theory mean?

As a noun, a theory is a group of tested general propositions “commonly regarded as correct, that can be used as principles of explanation and prediction for a class of phenomena .” This is what is known as a scientific   theory , which by definition is “an understanding that is based on already tested data or results .” Einstein’s theory of relativity and the  theory of evolution are both examples of such tested propositions .

Theory is also defined as a proposed explanation you might make about your own life and observations, and it’s one “whose status is still conjectural and subject to experimentation .” For example:  I’ve got my own theories about why he’s missing his deadlines all the time.  This example refers to an idea that has not yet been proven.

There are other uses of the word theory as well.

  • In this example,  theory is “a body of principles or theorems belonging to one subject.” It can be a branch of science or art that deals with its principles or methods .
  • For example: when she started to follow a new parenting theory based on a trendy book, it caused a conflict with her mother, who kept offering differing opinions .

First recorded in 1590–1600, theory originates from the Late Latin theōria , which stems from the Greek theōría. Synonyms for theory include approach , assumption , doctrine , ideology , method , philosophy , speculation , thesis , and understanding .

What does hypothesis mean?

Hypothesis is a noun that means “a proposition , or set of propositions, set forth as an explanation” that describe “some specified group of phenomena.” Sounds familiar to theory , no?

But, unlike a theory , a scientific  hypothesis is made before testing is done and isn’t based on results. Instead, it is the basis for further investigation . For example: her working hypothesis is that this new drug also has an unintended effect on the heart, and she is curious what the clinical trials  will show .

Hypothesis also refers to “a proposition assumed as a premise in an argument,” or “mere assumption or guess.” For example:

  • She decided to drink more water for a week to test out her hypothesis that dehydration was causing her terrible headaches.
  • After a night of her spouse’s maddening snoring, she came up with the hypothesis that sleeping on his back was exacerbating the problem.

Hypothesis was first recorded around 1590–1600 and originates from the Greek word hypóthesis (“basis, supposition”). Synonyms for hypothesis include: assumption , conclusion , conjecture , guess , inference , premise , theorem , and thesis .

How to use each

Although theory in terms of science is used to express something based on extensive research and experimentation, typically in everyday life, theory is used more casually to express an educated guess.

So in casual language,  theory and hypothesis are more likely to be used interchangeably to express an idea or speculation .

In most everyday uses, theory and hypothesis convey the same meaning. For example:

  • Her opinion is just a theory , of course. She’s just guessing.
  • Her opinion is just a hypothesis , of course. She’s just guessing.

It’s important to remember that a scientific   theory is different. It is based on tested results that support or substantiate it, whereas a hypothesis is formed before the research.

For example:

  • His  hypothesis  for the class science project is that this brand of plant food is better than the rest for helping grass grow.
  • After testing his hypothesis , he developed a new theory based on the experiment results: plant food B is actually more effective than plant food A in helping grass grow.

In these examples, theory “doesn’t mean a hunch or a guess,” according to Kenneth R. Miller, a cell biologist at Brown University. “A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

So if you have a concept that is based on substantiated research, it’s a theory .

But if you’re working off of an assumption that you still need to test, it’s a hypothesis .

So remember, first comes a hypothesis , then comes theory . Now who’s ready for a  Big Bang Theory marathon?

Now that you’ve theorized and hypothesized through this whole article … keep testing your judgment (Or is it judgement?). Find out the correct spelling here!

Or find out the difference between these two common issues below!

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

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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 research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

  • A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
  • It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
  • A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
  • Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
  • For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
  • Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.
Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

  • Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

hypothesis

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.
  • Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
  • However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

  • Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
  • Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
  • Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
  • Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
  • Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

  • The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
  • The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

  • Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
  • Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
  • Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
  • Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
  • Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
  • Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
  • Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
  • Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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1.3: Hypothesis, Theories, and Laws

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Learning Objectives

  • Describe the difference between hypothesis, theory as scientific terms.
  • Describe the difference between a theory and scientific law.

Although all of us have taken science classes throughout the course of our study, many people have incorrect or misleading ideas about some of the most important and basic principles in science. We have all heard of hypotheses, theories, and laws, but what do they really mean? Before you read this section, think about what you have learned about these terms before. What do these terms mean to you? What do you read contradicts what you thought? What do you read supports what you thought?

What is a Fact?

A fact is a basic statement establish by experiment or observation. All facts are true under the specific conditions of the observation.

What is a Hypothesis?

One of the most common terms used in science classes is a "hypothesis". The word can have many different definitions, depending on the context in which it is being used:

  • "An educated guess" - because it provides a suggested solution based on evidence to be a scientific hypothesis
  • Prediction - if you have ever carried out a science experiment, you probably made this type of hypothesis, in which you predicted the outcome of your experiment.
  • Tentative or Proposed explanation - hypotheses can be suggestions about why something is observed, but in order for it to be scientific, we must be able to test the explanation to see if it works, if it is able to correctly predict what will happen in a situation, such as: if my hypothesis is correct, we should see ___ result when we perform ___ test.
A hypothesis is very tentative; it can be easily changed.

What is a Theory?

The United States National Academy of Sciences describes what a theory is as follows:

"Some scientific explanations are so well established that no new evidence is likely to alter them. The explanation becomes a scientific theory. In everyday language a theory means a hunch or speculation. Not so in science. In science, the word theory refers to a comprehensive explanation of an important feature of nature supported by facts gathered over time. Theories also allow scientists to make predictions about as yet unobserved phenomena."

"A scientific theory is a well-substantiated explanation of some aspect of the natural world, based on a body of facts that have been repeatedly confirmed through observation and experimentation. Such fact-supported theories are not "guesses" but reliable accounts of the real world. The theory of biological evolution is more than "just a theory." It is as factual an explanation of the universe as the atomic theory of matter (stating that everything is made of atoms) or the germ theory of disease (which states that many diseases are caused by germs). Our understanding of gravity is still a work in progress. But the phenomenon of gravity, like evolution, is an accepted fact."

Not some key features of theories that are important to understand from this description:

  • Theories are explanations of natural phenomenon. They aren't predictions (although we may use theories to make predictions). They are explanations why we observe something.
  • Theories aren't likely to change. They have so much support and are able to explain satisfactorily so many observations, that they are not likely to change. Theories can, indeed, be facts. Theories can change, but it is a long and difficult process. In order for a theory to change, there must be many observations or evidence that the theory cannot explain.
  • Theories are not guesses. The phrase "just a theory" has no room in science. To be a scientific theory carries a lot of weight; it is not just one person's idea about something
Theories aren't likely to change.

What is a Law?

Scientific laws are similar to scientific theories in that they are principles that can be used to predict the behavior of the natural world. Both scientific laws and scientific theories are typically well-supported by observations and/or experimental evidence. Usually scientific laws refer to rules for how nature will behave under certain conditions, frequently written as an equation. Scientific theories are more overarching explanations of how nature works and why it exhibits certain characteristics. As a comparison, theories explain why we observe what we do and laws describe what happens.

For example, around the year 1800, Jacques Charles and other scientists were working with gases to, among other reasons, improve the design of the hot air balloon. These scientists found, after many, many tests, that certain patterns existed in the observations on gas behavior. If the temperature of the gas is increased, the volume of the gas increased. This is known as a natural law. A law is a relationship that exists between variables in a group of data. Laws describe the patterns we see in large amounts of data, but do not describe why the patterns exist.

What is a Belief?

A statement that is not scientifically provable. Beliefs may or may not be incorrect; they just are outside the realm of science to explore.

Laws vs. Theories

A common misconception is that scientific theories are rudimentary ideas that will eventually graduate into scientific laws when enough data and evidence has been accumulated. A theory does not change into a scientific law with the accumulation of new or better evidence. Remember, theories are explanations and laws are patterns we see in large amounts of data, frequently written as an equation. A theory will always remain a theory; a law will always remain a law.

Video \(\PageIndex{1}\): What’s the difference between a scientific law and theory?

  • A hypothesis is a tentative explanation that can be tested by further investigation.
  • A theory is a well-supported explanation of observations.
  • A scientific law is a statement that summarizes the relationship between variables.
  • An experiment is a controlled method of testing a hypothesis.

Contributors and Attributions

Marisa Alviar-Agnew  ( Sacramento City College )

Henry Agnew (UC Davis)

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

hypothesis about theory

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

hypothesis about theory

Verywell / Alex Dos Diaz

  • The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

  • Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

  • Forming a question
  • Performing background research
  • Creating a hypothesis
  • Designing an experiment
  • Collecting data
  • Analyzing the results
  • Drawing conclusions
  • Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

  • Is your hypothesis based on your research on a topic?
  • Can your hypothesis be tested?
  • Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

  • Collect as many observations about a topic or problem as you can.
  • Evaluate these observations and look for possible causes of the problem.
  • Create a list of possible explanations that you might want to explore.
  • After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

  • Does your hypothesis focus on something that you can actually test?
  • Does your hypothesis include both an independent and dependent variable?
  • Can you manipulate the variables?
  • Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

  • Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
  • Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
  • Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
  • Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
  • Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
  • Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

  • "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
  • Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
  • "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

  • "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
  • "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

  • "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
  • "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

  • "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
  • "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

  • "Staying up late will lead to worse test performance the next day."
  • "People who consume one apple each day will visit the doctor fewer times each year."
  • "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

  • The research question
  • The independent variable (IV)
  • The dependent variable (DV)
  • The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

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

ESLBUZZ

Hypothesis vs. Theory: A Simple Guide to Tell Them Apart

By: Author ESLBUZZ

Posted on Last updated: July 27, 2023

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Hypothesis and theory are no stranger to those who conduct studies and work in science. These two terms are often used interchangeably by non-researchers, but they have distinct meanings in the scientific community. Understanding the difference between a hypothesis and a theory is essential for anyone interested in scientific research or critical thinking.

In this article, we will explore the differences between hypothesis and theory and provide examples to help you understand how they are used in scientific research. We will also discuss the importance of these terms in the scientific method and how they contribute to our understanding of the natural world. Whether you are a student, a researcher, or simply someone interested in science, this article will provide valuable insights into the world of scientific research.

To help illustrate the differences between hypothesis and theory, we will provide a comparison table that summarizes the key differences between these two terms and examples of how scientists use hypotheses and theories to explain natural phenomena and make predictions about future events. By the end of this article, you will have a clear understanding of the differences between hypothesis and theory and how they are used in scientific research.

Hypothesis vs. Theory

Hypothesis vs. Theory: A Simple Guide to Tell Them Apart

Hypothesis vs. Theory: Definitions

Understanding hypothesis.

A hypothesis is an educated guess or assumption that is made before conducting research. It is a tentative explanation for a phenomenon or observation that is based on limited evidence or prior knowledge. In other words, a hypothesis is a statement that proposes a relationship between two or more variables, which can be tested through further investigation.

Characteristics of Hypothesis

Hypotheses have certain characteristics that set them apart from other types of statements. These characteristics include:

  • Testable: A hypothesis must be testable through empirical research. This means that it must be possible to collect data that can either support or refute the hypothesis.
  • Specific: A hypothesis must be specific in its predictions. It should clearly state what is expected to happen and under what conditions.
  • Falsifiable: A hypothesis must be falsifiable, which means that it must be possible to disprove the hypothesis if it is not supported by the evidence.
  • Parsimonious: A hypothesis should be simple and straightforward. It should not include unnecessary assumptions or variables.

Examples of Hypothesis

Here are some examples of hypotheses:

  • If a plant is exposed to sunlight, then it will grow faster than a plant that is not exposed to sunlight.
  • If a person consumes more calories than they burn, then they will gain weight.
  • If students are given more time to study for an exam, then they will perform better on the exam.

In summary, a hypothesis is an educated guess or assumption that is made before conducting research. It is testable, specific, falsifiable, and parsimonious. Examples of hypotheses include statements that propose a relationship between two or more variables, which can be tested through further investigation.

Understanding Theory

Definition of Theory

In scientific terms, a theory is a well-substantiated explanation of some aspect of the natural world that is based on empirical evidence. It is a collection of ideas that have been tested and confirmed through observation and experimentation. A theory is a framework that explains how and why things work in a certain way. It is a set of principles that can be used to make predictions about future events.

Characteristics of Theory

A theory has several characteristics that distinguish it from other scientific concepts such as hypotheses or laws. Some of the key characteristics of a theory are:

  • A theory is based on empirical evidence and is supported by multiple lines of evidence.
  • A theory is constantly evolving and can be modified or refined as new evidence emerges.
  • A theory is generally accepted as true by the scientific community and is widely used to make predictions and guide research.
  • A theory is not a guess or a hunch, but a well-substantiated explanation that has been rigorously tested.

Examples of Theory

There are many examples of well-established theories in science. Here are a few examples:

In summary, a theory is a well-substantiated explanation of some aspect of the natural world that is based on empirical evidence. It is a framework that explains how and why things work in a certain way and is constantly evolving as new evidence emerges. Theories are widely accepted as true by the scientific community and are used to make predictions and guide research.

Hypothesis vs. Theory: The Distinctions

As a writer, it is important to understand the differences between a hypothesis and a theory. These two scientific terms are often used interchangeably, but they have drastically different meanings in the world of science. In this section, we will explore the process of formulation, level of proof, and usage in the scientific community.

Process of Formulation

A hypothesis is an educated guess or assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. Hypotheses are often based on observations or previous research and can be either proven or disproven through experimentation.

On the other hand, a theory is a well-established principle that is formed to explain the things already shown in data. Theories are based on a large body of evidence and have been extensively tested and proven through experimentation. The formulation of a theory requires a lot of research, experimentation, and analysis.

Level of Proof

The level of proof required for a hypothesis and a theory is vastly different. A hypothesis requires a certain level of proof to be considered valid, but it can still be disproven through experimentation. In contrast, a theory has been extensively tested and proven through experimentation, and therefore requires a much higher level of proof to be disproven.

Usage in Scientific Community

In the scientific community, hypotheses and theories play different roles. Hypotheses are used to generate predictions and testable explanations for phenomena, while theories are used to explain and predict a wide range of phenomena. Hypotheses are usually the starting point for research, while theories are the end result of extensive research and experimentation.

To summarize, a hypothesis is an educated guess or assumption made before any research has been done, while a theory is a well-established principle that is formed to explain the things already shown in data. Hypotheses require a certain level of proof to be considered valid, while theories require a much higher level of proof. In the scientific community, hypotheses are used to generate predictions and testable explanations for phenomena, while theories are used to explain and predict a wide range of phenomena.

Hypothesis vs. Theory: Common Misconceptions

When it comes to scientific research, there are several misconceptions about the differences between hypothesis and theory. In this section, we’ll explore some of the most common misconceptions and clarify the differences between these two scientific terms.

Misconception #1: Hypotheses are less important than theories

One common misconception is that hypotheses are less important than theories. This is not true. A hypothesis is the foundation of scientific research, as it is a proposed explanation for an observation or phenomenon. Without a hypothesis, there can be no scientific investigation.

Misconception #2: Hypotheses are guesses

Another common misconception is that hypotheses are guesses. While a hypothesis is an educated guess, it is not a random or arbitrary guess. A hypothesis is based on prior knowledge, observations, and data. It is a proposed explanation that can be tested through experimentation.

Misconception #3: Theories are proven facts

Many people believe that theories are proven facts. This is not true. A theory is a well-substantiated explanation for a set of observations or phenomena. It is based on a large body of evidence and has been repeatedly tested and confirmed through experimentation. However, theories are not absolute truths and are subject to revision or rejection based on new evidence.

Misconception #4: Hypotheses become theories

Some people believe that hypotheses become theories once they are proven. This is not true. A hypothesis can be supported or rejected by experimental evidence, but it does not become a theory. A theory is a broader explanation that encompasses many hypotheses and has been extensively tested and confirmed.

Misconception #5: Theories are more certain than hypotheses

Another common misconception is that theories are more certain than hypotheses. While theories are based on a large body of evidence and have been extensively tested, they are not absolute truths. Theories are subject to revision or rejection based on new evidence, just like hypotheses.

In summary, hypotheses and theories are both important components of scientific research. Hypotheses are proposed explanations that can be tested through experimentation, while theories are well-substantiated explanations that have been extensively tested and confirmed. While there are many misconceptions about the differences between hypotheses vs. theory, understanding these differences is crucial for conducting scientific research.

In conclusion, while the terms “hypothesis” and “theory” are often used interchangeably, they have distinct differences in the scientific method. A hypothesis is an assumption made before any research has been done, formed so that it can be tested to see if it might be true. On the other hand, a theory is a principle formed to explain the things already shown in data.

One way to differentiate between a hypothesis and a theory is to consider the level of evidence supporting each. A hypothesis is a proposed explanation for a phenomenon, but it is not yet supported by sufficient evidence. In contrast, a theory is a well-established explanation for a phenomenon that has been supported by a large body of evidence.

Another way to differentiate between a hypothesis and a theory is to consider their role in the scientific method. A hypothesis is an initial step in the scientific method, where a researcher formulates a testable prediction about a phenomenon. A theory, on the other hand, is the end result of the scientific method, where a researcher has tested and confirmed a hypothesis over time.

It is important to note that a hypothesis can eventually become a theory if it is repeatedly tested and supported by evidence. However, a theory can never become a hypothesis, as it is already a well-established explanation for a phenomenon.

In summary, understanding the differences between hypothesis and theory is crucial for conducting and interpreting scientific research. By using these terms correctly, researchers can communicate their ideas clearly and accurately, contributing to the advancement of scientific knowledge.

Frequently Asked Questions

How can you distinguish between hypothesis and theory?

A hypothesis is an educated guess or a proposed explanation for an observation or phenomenon. It is a tentative explanation that can be tested through experiments and observations. On the other hand, a theory is a well-established explanation that has been supported by a large body of evidence. The main difference between a hypothesis and a theory is that a hypothesis is a proposed explanation that needs to be tested, while a theory is a well-supported explanation that has been tested and confirmed by multiple lines of evidence.

What is the difference between a theory and a hypothesis in biology?

In biology, a hypothesis is a proposed explanation for a biological phenomenon that can be tested through experiments and observations. For example, a biologist might propose a hypothesis to explain why a particular species of bird has a particular beak shape. A theory in biology, on the other hand, is a well-established explanation that has been supported by a large body of evidence. For example, the theory of evolution is a well-established explanation for the diversity of life on Earth.

What is an example of a theory statement?

A theory statement is a statement that summarizes a well-established explanation for a phenomenon. For example, the theory of relativity is a statement that summarizes Einstein’s well-established explanation for the behavior of objects in space and time.

How are hypotheses and theories similar and different?

Both hypotheses and theories are proposed explanations for phenomena. However, while hypotheses are tentative and need to be tested, theories are well-established and have been supported by a large body of evidence. In addition, hypotheses are often specific to a particular observation or phenomenon, while theories are more general and can explain a wide range of phenomena.

What are some examples of the differences between a hypothesis and a theory?

An example of a hypothesis might be that a particular drug will cure a particular disease. An example of a theory might be the theory of plate tectonics, which explains the movement of the Earth’s crust. The main difference between these two examples is that the first is a tentative explanation that needs to be tested, while the second is a well-established explanation that has been supported by a large body of evidence.

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"}},{"@type":"Question","name":"What is an example of a theory statement?","acceptedAnswer":{"@type":"Answer","text":"

A theory statement is a statement that summarizes a well-established explanation for a phenomenon. For example, the theory of relativity is a statement that summarizes Einstein's well-established explanation for the behavior of objects in space and time.

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"}},{"@type":"Question","name":"What are some examples of the differences between a hypothesis and a theory?","acceptedAnswer":{"@type":"Answer","text":"

An example of a hypothesis might be that a particular drug will cure a particular disease. An example of a theory might be the theory of plate tectonics, which explains the movement of the Earth's crust. The main difference between these two examples is that the first is a tentative explanation that needs to be tested, while the second is a well-established explanation that has been supported by a large body of evidence.

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  • Published: 03 March 2024

A dynamic panel threshold model analysis on heterogeneous environmental regulation, R&D investment, and enterprise green total factor productivity

  • Rong Ren 2 ,
  • Kaiyuan Cui 3 &
  • Lei Song 4  

Scientific Reports volume  14 , Article number:  5208 ( 2024 ) Cite this article

Metrics details

  • Environmental economics
  • Sustainability

Environmental regulations are important means to influence manufacturing enterprise green development. However, there are two completely different conclusions both in theoretical and in empirical research, namely the “Follow Cost” theory and the “Porter Hypothesis”. The nonlinear mechanism needs to be considered. Therefore, this study aims to explain the threshold impact of heterogeneous environmental regulations on enterprise green total factor productivity. Environmental regulations are divided into different sub-categories, then based on the panel data of 1220 Chinese manufacturing listed companies from 2011 to 2020, this paper uses threshold regression model to examine the impact of heterogeneous environmental regulations on Chinese manufacturing enterprise Green Total Factor Productivity. The empirical results show that: (1) Command-controlled, market-incentive and voluntary-agreement environmental regulation all have a significant nonlinear impact on enterprise Green Total Factor Productivity. (2) Enterprise R&D investment plays a threshold role in the impact. (3) There are industry and equity type differences in the impact process. This study focuses on the micro level of enterprises and tests the threshold mechanism, which make some theoretical complement to previous researches. The research results are not only beneficial for the government to propose appropriate environmental regulatory policies, but also for enterprises to achieve green growth through heterogeneous R&D investment.

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Introduction

Since the implementation of the reform and opening-up policy in 1978, China’s economy has developed rapidly. However, the development of Chinese economy has occurred at the expense of the environment 1 , 2 . That is, the traditional extensive economic development mode characterized by “high growth and high energy consumption” has increased the burden on China’s ecological environment, further led to increasingly prominent environmental problems, and become an obstacle to the economy’s sustainable development 3 . According to the BP World Energy Statistical Yearbook (2022), in 2021, CO 2 emissions generated by energy in China are 10.523 billion tons, accounts for 31.06% of the whole world, far more than other regions. At the same time, the global economy has shown the trend of low-carbon emission and green sustainable development. The implementation of the Paris Agreement has further established a clear agenda for global carbon reduction. Therefore, carbon emissions reduction is an important and arduous task both for environmental remediation in China and for promoting green development throughout the world 4 . GTFP, as a combination strategy under the dual goals of economic development and environmental protection, helps to promote economy’s sustainable development.

Manufacturing enterprises are both micro entities and carbon emitting entities of the national economy. According to the Carbon Emission Ranking of Chinese Listed Companies, the total carbon emissions of the 100 listed companies in 2022 were 5.046 billion tons, accounting for approximately 45.87% of China’s total carbon emissions. Obviously, manufacturing enterprises play an important role in achieving green and sustainable development. Green total factor productivity (GTFP) takes into account energy loss and pollution emissions in the production process, and is a comprehensive indicator for measuring economic performance and ecological environment performance. GTFP of manufacturing enterprises is the core and starting point for achieving broader economic and environmental goals. Therefore, it is of great significance to enhance GTFP of Chinese manufacturing enterprises 5 .

Enterprises are profit oriented economic organizations, the market economy, as a powerful engine of human development, has important imperfections 6 , thus relying solely on the invisible hand of the market is difficult to promote its spontaneous green production. Government policies play an important role in promoting sustainable technological progress and environmental sustainability 7 , 8 . Environmental regulations have important impacts on manufacturing enterprises GTFP 9 , 10 , 11 Thus, the Chinese government has utilized heterogeneous environmental regulations to promote enterprises green development. On one hand, a series of command-controlled environmental regulations have been made, including the Environmental Protection Law of China, the Cleaner Production Promotion Law in China, Atmospheric Pollution Prevention and Treatment Law in China etc. On the other hand, some market-incentive environmental regulations such as carbon emission quotas are used to encourage manufacturing enterprises green development.

Governments around the world actively formulate environmental regulations, hoping to promote green development. And related researches on the impact of environmental regulations on GTFP has attracted great attention. However, there are two completely different views both in theoretical and empirical, “the driving role of environmental regulations” and “the hindering role of environmental regulations” 12 , 13 , 14 . According to the “Follow Cost” theory, the stricter environmental regulations are, the higher cost of enterprises pollution controls are, which will furthermore restrain the production efficiency and profitability of enterprises, and hinder the improvement of enterprises GTFP 15 . However, according to the theory of “Porter Hypothesis”, moderate environmental regulations can encourage enterprises to engage in more innovative activities, which will increase their productivity, offset the investment cost caused by environmental regulations and enhance their profitability in the market 16 , 17 .

Empirical research also has completely different conclusions. Some empirical results have found that environmental regulations can improve both industry production efficiency and environmental performance by influencing technological progress, thereby achieving green growth 18 . Some other empirical results have showed that environmental regulations have caused cost increases, and their contribution to technological innovation is relatively small or even inhibitory, which is not conducive to the improvement of production efficiency and environmental performance, and thus hinders green growth 19 . There are also some empirical researches have confirmed that there is a non-linear relationship or even no correlation between environmental regulations and green development 16 .

Theoretical and empirical research have not yet reached a consensus, and there are two possible reasons. Firstly, the existing researches on the impact of environmental regulations on green growth mainly focus at the regional or industry level, related researches at the level of enterprise is insufficient 15 . In fact, both the “Following Cost” theory and the “Porter Hypothesis” have their rationality, and the reason of contradiction lies in the coexistence of cost increase and innovation compensation caused by environmental regulations, but there is insufficient discussion on the boundary conditions. As heterogeneous individuals, enterprises may have quite different reactions to the same environmental regulatory policies. And only the exploration that focuses on the micro level of enterprises may open this “black box”. Secondly, environmental regulations are a comprehensive concept that can be further divided into different subcategories, which may lead to inconsistent research conclusions if confusing them together. Therefore, it is necessary to explore the mechanism of environmental regulations affecting GTFP of heterogeneous enterprises at micro level. Thirdly, both theoretical and empirical researches have shown that the impact of environmental regulations on green development may be non-linear. Environmental regulations only generate external conditions that can affect enterprises’ behavior. Whether and to what extent an enterprise innovates is determined by enterprise heterogeneity factors, and R&D investment is an important determining factor. Therefore, this article uses enterprise R&D investment as a threshold variable to explore the threshold impact of heterogeneous environmental regulations on enterprise GTFP.

The contribution of this paper mainly lies in the following: (1) Focusing on the micro level of enterprises, this paper divides environmental regulations into three heterogeneous subcategories including command-controlled type, market-incentive type and voluntary-agreement type, and furthermore clarify the mechanism of heterogeneous environmental regulations affecting enterprise GTFP; (2) Focusing on the micro level of enterprises, enterprise R&D investment is introduced as the threshold variable to clarify the panel threshold mechanism and determine the threshold values; (3) Heterogeneity analysis is conducted based on enterprise industry type and enterprise equity type. The research conclusions can provide theoretical reference and decision-making reference both for enterprises from micro level and for policy makers from macro level. The research framework of this paper is illustrated in Fig.  1 .

figure 1

Analytical framework of this study.

Literature review and hypothesis

Heterogeneity environmental regulations and enterprise gtfp.

The research on the impact of environmental regulations on GTFP has become a fore issue, and different impact direction of the empirical results have also spawned further thinking of scholars. Firstly, researchers propose that environmental regulations should be divided into different subcategories, and the impact of heterogeneous environmental regulations on GTFP are different 20 , 21 . Researchers conducted deeper discussions on heterogeneous environmental regulations, and most agree that environmental regulation can be divided into three subtypes, which are command-controlled environmental regulation (CER), market-incentive environmental regulation (MER) and public-participation environmental regulation (VER) 22 . Secondly, the existing researches mainly focuses on the regional and industry levels, and there is still insufficient research on the enterprise level 15 . Thirdly, limited researches on enterprise green growth mainly focuses on enterprise green patents. On the one hand, this calculation doesn’t consider input–output ratio can easily lead to biased research conclusions. On the other hand, it cannot verify the impact mechanism on many enterprises without green patents. The traditional methods used to measure productivity growth ignore the pollutants that are produced by the production process, while GTFP takes into account pollutant emissions during the production process, and measures enterprise green growth from the perspective of input-output 7 . Thus, GTFP can not only include all enterprises but also better reflect enterprise green growth efficiency.

Undoubtedly, environmental regulation is one of the most important forces in achieving green growth 23 . Command-controlled environmental regulation (CER) refers to the government guiding and standardizing the production process of enterprises through a series of administrative means, such as environmental regulations, rules, policies, emission standards and so on. On the one hand, the strict CER given by the government will inevitably increase enterprise environmental governance costs and then lead to excessive environmental governance expenditures in the short term, which is unfavorable for enterprise green growth 24 . The research utilizing a comprehensive CS-ARDL model and using data of OECD countries from 1990 to 2020 found that environmental policies are effective in reducing carbon dioxide emissions 25 . The empirical study based on the panel data of China’s 31 energy-mineral cities in 2007–2018 showed that CER has an inhibitory impact on energy eco-efficiency, and the inhibitory effect is more obvious in central and northeastern regions 26 . Research adopted the Spatial Durbin Model found that CER will hinder regional green technology innovation 27 . On the other hand, CER will significantly increase enterprise environmental legal costs of production and operation process, thereby playing a deterrent role and help to promote enterprise technological innovation, and achieving green growth 15 . Based on panel data from 30 provinces in China from 2003 to 2017, the results of Systematic Generalized Method of Moments indicated that CER has a significant promoting effect on green innovation 28 . The empirical study on the data of Chinese A-share companies listed from 2010 to 2019 found that CER can stimulate enterprise green technology innovate 29 . Besides, CER may has a nonlinear impact on green development. Some research suggested that CER has a significant threshold impact on green technological innovation 30 , and when its intensity exceeds a certain threshold, green technology innovation is improved. Similar researches found that with the improvement of economic level, environmental regulation shows a “U” relationship of first restraining and then promoting technological innovation 31 .

From the above analysis, it can be seen that both theoretical and empirical studies have not reached a consensus on CER’s impact on GTFP, and threshold effect may exist. Thus, Hypothesis 1 is proposed.

Hypothesis 1

CER has a threshold effect on enterprise GTFP.

Market-incentive environmental regulation (MER) mainly refers to market-oriented institutional regulatory measures especially economic means such as pollution control investment, environmental protection fund investment and other tax standards, aim to guide enterprises achieving green production 32 .

Theoretical research has not reached a consensus on MER’s impact on GTFP. On the one hand, there is a divergence between the “Follow Cost” theory and the “Porter Hypothesis”. On the other hand, even the “Porter Hypothesis” suggested that only those carefully designed environmental regulations can promote green development by enhancing innovation. That is, the mechanism between carefully designed regulation and its potential innovation offsetting effects is not yet clear 33 .

The empirical research results are also quite different. Gray and Shadbegian found that higher pollution abatement costs significantly decrease the productivity 34 . According to the panel data of A-share new energy companies listed in Shanghai and Shenzhen from 2012 to 2020, the research drew the conclusion that MER could promote the green innovation of new energy firms 35 . Based on the data of 30 provinces in China during the period of 2000 to 2012, the empirical results showed that environmental expenditure as one type of MER has a U-shaped impact on green productivity growth of China’s industry 15 . Some scholars pointed that when the intensity of MER is not high, the environmental cost of enterprises is relatively low, so enterprises lack the power of technological innovation and thus prevent the improvement of GTFP, when the intensity of MER is high enough, enterprises have a strong driving force for technological innovation, and then promote the improvement of GTFP 36 , 37 . Based on the above analysis, the impact of MER on GTFP is relatively complex and threshold effect may exist. Thus, Hypothesis 2 is proposed.

Hypothesis 2

MER has a threshold effect on enterprise GTFP.

Voluntary-agreement environmental regulation (VER) also known as public-participation based environmental regulation, refers to the regulation of corporate behavior through external supervision by individuals or non-governmental organizations, such as the number of environmental petitions, batches of environmental petitions, and the number of environmental petitions. The public could actively perform duties of environmental protection through environmental letters or visits and media supervision, reflect enterprise environmental pollution problems to the government, so as to supervise enterprise pollution behaviors 38 . Therefore, it conveys an implicit message to the outside world, that is, the enterprise has social responsibility and pays attention to environmental protection 33 , 39 . A good corporate image in turn helps enterprise attract more external investment and provides support for its green technological innovation. According to the research of Jia 40 , the greater VER is, the more enterprises will be encouraged to take more measures to pursue environmental performance, and therefore has a positive impact on GTFP. Taking A-shared listed enterprises in China from 2010 to 2019 as samples, the empirical results showed that VER inhibited enterprise green technological innovation 29 . Based on panel data of 86 Chinese steel enterprises from 2005 to 2014, the paper analyzed that VER has a direct and positive effects on the enterprise technological innovation 41 .

Based on the above analysis, the impact of VER on GTFP may exist a threshold effect. Thus, Hypothesis 3 is proposed.

Hypothesis 3

VER has a threshold effect on enterprise GTFP.

R&D investment and enterprise GTFP

The important mechanism by which environmental regulations affect enterprise GTFP is to promote the internalization of enterprise environmental governance costs, so as to stimulate enterprise innovation activities, and then affect enterprise GTFP 6 . However, it is still unclear for the mechanism between the cost increase caused by environmental regulations and possible innovation compensation 33 . The heterogeneity of enterprise, especially enterprises differentiated innovation level became an important determining factor that affects the driving effect. R&D investment is an important indicator to measure the level of innovation 6 . The environmental regulations’ driving effect is mainly achieved by influencing enterprise R&D investment. Therefore, it is necessary to further clarify the impact of enterprise R&D investment on GTFP from the enterprise level.

According to the Theory of Endogenous Growth, innovation can form a new production function, and technological progress driven by knowledge spillovers and knowledge transfer is the decisive factor for sustainable growth 6 . R&D investment is the source of enterprise innovation and economic growth. R&D investment is conducive to optimizing production factors and reducing information asymmetry, this not only expand the source of explicit knowledge, but also increase organization flexibility, which can further promote the spillover and absorption of tacit knowledge, and then improve enterprise performance. R&D promotes environmental protection by reducing CO 2 emissions 25 . Based on the panel data of high-tech enterprises in China from 2012 to 2017, the research proposed that innovation investment plays a mediating role in the impact of heterogeneous environmental regulations on enterprise innovation 42 .

The “Follow Cost” theory suggests that environmental regulations will lead to an increase in enterprises pollution control costs. Especially in the short term, constraints by capital amounts, enterprises have to increase the pollution discharge investment which will inevitably lead to a decrease in R&D investment and resulting “R&D Crowding Out Effect” 43 . However, the “Porter Hypothesis” suggests that in the long term, reasonable and strict environmental regulations can promote technological practices. The costs of environmental regulations may be partially or entirely offset by the compensating effects of innovation activities, and thus environmental regulations can promote GTFP through the “Innovation Compensation Effect” 44 .

The magnitude of the “R&D Crowding Out Effect” and “Innovation Compensation Effect” affects the performance of environmental regulatory policy. Enterprise R&D investment is an important factor affecting R&D costs and long-term technological innovation capabilities, and may have a significant impact on heterogeneous environmental regulations affecting enterprise green development 29 .

The continuous developing of relevant research has laid the foundation and provided a new perspective for further exploring the impact of heterogeneous environmental regulations’ innovation driving effect. Therefore, this paper introduces enterprises R&D investment in the analysis, and believes that enterprises R&D investment plays an important role in the impact of heterogeneous environmental regulations on enterprises GTFP 45 , 46 .

Based on the above analysis, Hypothesis 4 , Hypothesis 5 and Hypothesis 6 are proposed.

Hypothesis 4

Enterprise R&D investment plays a threshold role in the impact of CER on enterprises GTFP.

Hypothesis 5

Enterprise R&D investment plays a threshold role in the impact of MER on enterprises GTFP.

Hypothesis 6

Enterprise R&D investment plays a threshold role in the impact of VER on enterprises GTFP.

Research methodology

Sample selection.

The samples of this paper are panel data of 1220 A-share manufacturing listed companies in China. The relevant data is obtained from the China Stock Market and Accounting Research Database (CSMAR). Firstly, according to the industry classification codes in the Guidelines for Industry Classification of Listed Companies (2012 Revision) issued by the China Securities Regulatory Commission, 31 categories of A-share manufacturing listed companies are selected. Secondly, excluded enterprises that have not been listed or have been delisted during the study period, excluded enterprises that have been ST、*ST or samples with lots of missing data. Finally, 12,200 pieces of panel data of the sample enterprises from 2011 to 2020 are obtained.

Variables measurement and data sources

Dependent variables.

The dependent variable of this paper is sample enterprises GTFP. Scholars have used several methods to measure green productivity issues, such as descriptive analysis, non-parametric analysis including data envelope analysis, input–output analysis, dynamic computable general equilibrium, parametric analysis based on a combined method of parametric analysis 7 . This article constructs the optimal frontier of enterprises green growth based on the Super-SBM Model includes undesirable outputs, and calculates enterprises GTFP according to the Super-SBM Mode 47 , 48 , 49 . Specifically, the measurement of GTFP involves input factors, desirable output, and undesirable output.

Firstly, referring to related methods for measuring GTFP, this paper selects input indicators includes capital input, labor input, intermediate input, and energy input 4 , 50 . Referring to the general method, enterprise capital input is calculated by “the perpetual inventory method” based on the “net value of enterprise fixed assets”, enterprise labor input is calculated by “the cash paid by the enterprise to employees”, enterprise intermediate investment is calculated by “the sum of operational expense, sales expenses, financial expenses, and management expenses minus the cash paid to and for employees, and minus depreciation and amortization”, enterprise energy investment is calculated by “the proportion of enterprise gross output value to the gross industry output value, and multiply the energy consumption of the industry that enterprise belongs to”. Secondly, enterprise desirable output is calculated by “enterprise main business income”. Thirdly, enterprise undesirable output is calculated by “three kinds of enterprise’s industrial waste”, including the industrial wastewater, SO2, and industrial smoke emissions of enterprise. The relevant data comes from CSMAR, China Statistical Yearbook, China Environmental Statistical Yearbook, and China Urban Statistical Yearbook.

Independent variable

The independent variable of this article are heterogeneous environmental regulations, which includes three subcategories: CER, MER and VER. Referring to current calculation method, CER is calculated by “the number of environmental case proposals submitted by provincial and municipal people’s congresses” 26 . Investment of environmental pollution control as one of the MER method, can demonstrate the incentive costs invested by regional governments and continuous data can be obtained. Therefore, MER in this article is calculated by “the ratio of the completed investment in industrial pollution control in each region to the regional GDP” 50 , 51 , 52 , 53 . Considering previous research and data availability, VER is calculated by “the number of environmental petitions in each province and city” 29 . Relevant data are collected from China Environmental Statistical Yearbook.

Threshold variables

The threshold variable of this article is enterprise R&D investment (RD). Firstly, there is a close correlation among environmental regulations, R&D investment and GTFP. According to Porter Hypothesis, environmental regulations have innovation driven effects. In fact, environmental regulations only generate external conditions that can affect enterprises’ behavior. Whether and to what extent an enterprise innovates is determined by enterprise heterogeneity factors, and R&D investment is an important determining factor. Secondly, enterprise R&D investment can be clearly measured. Thirdly, data of R&D investment is reliable. R&D investment can be found in the annual reports of listed companies. The disclosure of this indicator is very comprehensive, with few missing items, and the data is available and reliable. Therefore, enterprise R&D is used this article investment as a threshold variable to explore the threshold impact of heterogeneous environmental regulations on enterprise GTFP. Enterprise R&D investment is measured by the proportion of enterprise R&D amount in sales revenue 35 . Relevant data is from CSMAR.

Control variables

Referring to relevant literatures on environmental regulations and enterprise green growth, the following indicators are selected as control variables 54 , 55 , 56 : enterprise digital level (DL), return on assets (ROA), return on equity (ROE), ratio of asset liability (ROL), and enterprise scale (Scale). Enterprise digital level is measured by “the number of digitization-related-words frequencies in enterprise annual reports” based on Python word frequency statistical analysis methods. Enterprise return on assets is measured by “the proportion of enterprise’s net profit in the total assets”. Enterprise return on equity is measured by “the proportion of enterprise’s net profit to net assets”. Enterprise ratio of asset liability is measured by “the proportion of its total liabilities to total assets”. Enterprise scale is measured by “enterprise total assets”.

Grouping variables

In order to test whether there is heterogeneity in the threshold regression results, referring to classic theories and considering enterprise most important heterogeneity, enterprise industry (Indus) and enterprise equity (Equ) type are selected as two grouping variables. Enterprise industry can be divided into three categories based on their pollution emission levels which are high pollution industry, medium pollution industry and low pollution industry. Enterprise equity can be divided into two categories based on the equity status which are state-owned and non-state-owned.

All the variables except for grouping and ratio variables are logarithmically processed after adding one. And the summary of variables is shown in Table 1 .

Model construction

Firstly, according to literature review, it was found that environmental regulations have both promoting and inhibiting effects on green development. The current research debate focuses on the conditions for a reasonable explanation of these two effects. Secondly, threshold regression models are non-linear models that are suitable for explaining the non-linear relationships between variables, especially the threshold effect that both positive and negative effects exist simultaneously. Thirdly, this article attempts to explore the threshold impact of heterogeneous environmental regulations on enterprise GTFP. The threshold regression model is very suitable to explain the mechanism that this article proposed.

Based on all the above theoretical analysis, in order to test the research hypotheses H1-H6 and find the optimal range of R&D investment that can maximize enterprise GTFP, the panel threshold model based on Hansen model 57 is shown as Eq. ( 1 ). In the equation, GTFP i,t represents enterprises green total factor productivity, I (*) is the indicator function, enterprise R&D investment is taken as the threshold variable and γ 1 、 γ 2 is the threshold of threshold of the effect. α 0 is constant terms, α 1 、α 2 、α 3 、ρ i are the regression parameters, θ i is the unobservable individual effect, ε i,t is the random error term. Other variables are the same as the above.

Results and analysis

Description and correlation analysis.

The descriptive statistics of variables and the correlation analysis results are shown in Table 2 . It can be seen from the table that the average values of GTFP is 0.698, reacting that many sample enterprises GTFP is not very high, the standard deviation of GTFP is 0.184, indicating that small differences in GTFP among sample enterprises. Among the three subcategory tools for environmental regulations, the standard deviation of MER is the minimum and the standard deviation of VER is maximum. In addition, the variance of most variables is less than the mean value, indicating that the dispersion coefficient is relatively small and the stability of the sample is good. The Pearson correlation analysis results are also shown in Table 2 . It can be seen that there is interdependence between variables, but it cannot distinguish the causal relationship of variables. Therefore, it is both possible and necessary to further establish quantitative relationships between variables through regression analysis.

Threshold effect test

Firstly, examine whether the threshold effect exists and determine the number of thresholds. The results are shown in Table 3 . According to the results, enterprise R&D investment is the threshold variable, CER, MER and VER all have significant dual threshold effects on enterprise GTFP. Double threshold effect diagram is shown in Fig.  2 .

figure 2

Double threshold effect diagram of the impact of heterogeneous environmental regulations on GTFP.

Further threshold regression analysis was conducted on three types of heterogeneous environmental regulations, and the overall results are shown in Tables 4 , 5 , and 6 .

Table 3 and the first column in Table 4 all show that CER has a significant dual threshold impact on enterprise GTFP. If RD < 3.79, CER has no significant impact on enterprise GTFP. If 3.79 ≤ RD < 9.21, CER has a significant positive impact on enterprise GTFP. If RD ≥ 9.21, CER also has a significant positive impact on enterprise GTFP, and the impact degree are enhanced. With the increase of enterprise R&D investment, the positive impact of CER on GTFP increases. The possible reason is that higher levels of R&D investment often incentive enterprise technological innovation, which is more suitable for strict CER. That is, with the increase of enterprise R&D investment, the stricter CER, the more conducive it is to guiding and promoting enterprises to carry out green production.

Table 3 and the first column in Table 5 all show that MER has a significant dual threshold impact on enterprise GTFP. If RD < 3.30, MER has a significant negative impact on enterprise GTFP. If 3.30 ≤ RD < 8.14, MER has no significant impact on enterprise GTFP. If RD ≥ 8.14, MER has a significant positive impact on enterprise GTFP. The results indicate that the impact of MER on enterprise GTFP presents a “U-shaped” pattern. Only when enterprise R&D investment exceeds the threshold value can MER be conducive to enterprise GTFP. Therefore, when enterprises face an increasing fierce of MER, they should try to increase R&D investment, which can help enterprises cross the threshold of negative impact, and stimulate MER’s promoting effect on GTFP.

Table 3 and the first column in Table 6 all show that VER has a significant dual threshold impact on enterprise GTFP. If RD < 3.79, VER has a significant negative impact on enterprise GTFP. If 3.79 ≤ RD < 8.14, VER has a significant negative impact on enterprise GTFP but the negative effect was reduced obviously. If RD ≥ 8.14, VER has no significant impact on enterprise GTFP. This result shows that the external environmental supervision mechanism increases enterprise environmental governance cost, which has a negative impact on enterprise GTFP in the short term, but this negative impact gradually weakens and tends to be positive with the increase of enterprise R&D investment. Thus, if companies want to avoid the negative impact of VER on GTFP, they should try to increase R&D investment as much as possible and cross the threshold of negative impact.

Heterogeneity analysis

Heterogeneity of industries.

The sample enterprises are subdivided into high pollution industries, medium pollution industries and low pollution industries to further discuss the threshold mechanism of heterogeneous environmental regulations affecting enterprise GTFP. The results are shown in Tables 4 , 5 , and 6 . Among them, Industry 1 represents high pollution industries, Industry 2 represents medium pollution industries, Industry 3 represents low pollution industries.

The regression results indicate that CER, MER, and VER all have threshold effects on enterprise GTFP, and the degree of impact varies across industries. Among them, CER has a more significant environmental driving effect on enterprises in highly polluting industries, which is conducive to promoting these enterprises to increase their GTFP. This also reflects that China’s current CER is moderately and effective. Overall, they have not increased the environmental governance burden on manufacturing enterprises in medium and low pollution industries, and are also effective regulations for excessive pollution of manufacturing enterprises in high pollution industries. MER increases the internal cost of enterprise pollution governance, so it has a negative impact on enterprise GTFP as a whole. VER has a stronger regulatory effect on high pollution enterprises.

Heterogeneity of ownership

The sample enterprises are further subdivided into state-owned and non-state-owned enterprises to discuss the heterogeneous threshold effect. The results are shown in Tables 4 , 5 , and 6 . Among them, State represents state-owned enterprises, Non-state represents non-state-owned enterprises.

The regression results indicate that CER, MER, and VER all have threshold effects on enterprise GTFP, and the degree of impact varies across equity structure. The impact on non-state-owned manufacturing enterprises is more significant.

Robustness tests

Robustness test is performed by two approaches. On the one hand, the Winsorizing method was made on every variable to test the robustness of research conclusions. Specifically, all continuous variables were Winsorized at the 1% and 99% levels to mitigate the potential impact of outliers on empirical results. On the other hand, threshold regression tests were re-conducted based on the dependent variable set one-period lag. The robustness test results are shown in Table 7 . The results show that all variables basically maintain the same impact direction and impact trend, and pass the significance test. The research conclusions are reliable and robust.

Conclusions and limitations

Conclusions and policy implications.

Based on the panel data of 1220 Chinese manufacturing listed companies from 2011 to 2020, this paper uses threshold regression model to examine the impact of heterogeneous environmental regulations on enterprise GTFP. Three main conclusions are drawn. (1) Heterogeneous environmental regulation has a double threshold impact on enterprise GTFP. Specifically, CER has a significant positive impact on enterprise GTFP, but the degree of impact decreases. MER has a significant “U-shaped” impact on enterprise GTFP. VER has a significant negative decreasing influence on enterprise GTFP. (2) Enterprise R&D investment plays a threshold role in the impact of heterogeneous environmental regulations on enterprise GTFP. The “Follow Cost” and “Porter Hypothesis” effects act at different stages. And these findings remain valid after a series of robustness tests. (3) There are industry and ownership differences in the impact of heterogeneous environmental regulations on GTFP. In general, environmental regulations have a more significant impact on enterprises in highly polluting industries and non-state-owned enterprises.

These conclusions have valuable policy implications for formulating flexible environmental regulations and promoting Chinese enterprises low-carbon development. Firstly, the empirical results show that environmental regulations are not the stricter the better. On the one hand, the environmental effects motivated by environmental regulations show significant dual threshold effect, and the trend of MER is most obviously. On the other hand, the environmental effects motivated by environmental regulations show industry and ownership differences. Thus, the government should develop a flexible environmental regulation system and prioritize the use of market-incentive environmental regulation measures. Secondly, the empirical results show that enterprises R&D investment determine the influence direction and degree. Therefore, manufacturing enterprises should rely on more R&D investment to decrease the “obstructive effects of environmental regulations”, and achieve green, low-carbon, and sustainable development while improving enterprise productivity simultaneously.

Limitations and future research

This paper still has some limitations which may also be directions for future research. Firstly, in view of limited data, “enterprises industrial wastes data” used in this study is to quantify the industrial wastewater, SO 2 and industrial smoke emissions at the city level to the enterprise level on a year-on-year basis through the “proportion of total output value of enterprises”. In future studies, data algorithms can be further improved to evaluate the effectiveness of heterogeneous environmental regulations on enterprises green growth. Secondly, this article mainly explores the threshold effect of heterogeneous environmental regulations on enterprise GTFP. Nowadays, some studies have pointed out that there is a two-way dynamic relationship between heterogeneous environmental regulations and green development 53 , and the environmental effects motivated by environmental regulations have spillover effects 7 . Therefore, further research can explore these aspects in depth. Thirdly, digitalization is booming worldwide attention, which has brought both opportunities and challenges to enterprises green development. Although enterprise digital level has been considered as a controllable variable in this article, we didn’t mainly discuss its effects. In the future, researches could conduct in-depth researches on the impact of enterprise digital level as a core variable on enterprise GTFP.

Data availability

Data will be made available on request.

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The research is supported by National Social Science Foundation of China (Grant No. 22BJL113) and Shandong Province Social Science Planning Project (No. 22CGLJ22).

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