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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

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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%).

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.

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Biology archive

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

• Controlled experiments
• The scientific method and experimental design

Introduction

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

Scientific method example: Failure to toast

1. make an observation., 2. ask a question., 3. propose a hypothesis., 4. make predictions., 5. test the predictions..

• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

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

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

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• Knowledge Base
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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, 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 variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

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 .

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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 identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise 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.

Step 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

Step 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.

Step 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 .

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.

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

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

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

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

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

Methodology

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

Research bias

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

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

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).

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.

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What is a scientific hypothesis?

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

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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What is and How to Write a Good Hypothesis in Research?

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• 344.4K views

Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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Systematic Literature Review or Literature Review?

How to Write an Effective Problem Statement for Your Research Paper

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "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."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. 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. At this point, researchers then 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 numerous 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 adage 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.

How to Formulate a Good Hypothesis

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.

The Importance of Operational Definitions

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.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

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 various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. 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. For example, 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.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

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 there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• 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 population sample 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."
• "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."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

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:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

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  conducting an experiment is difficult or impossible. 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 examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Theories, Hypotheses, and Laws: Definitions, examples, and their roles in science

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that the idea of evolution had been part of Western thought for more than 2,000 years before Charles Darwin was born? Like many theories, the theory of evolution was the result of the work of many different scientists working in different disciplines over a period of time.

A scientific theory is an explanation inferred from multiple lines of evidence for some broad aspect of the natural world and is logical, testable, and predictive.

As new evidence comes to light, or new interpretations of existing data are proposed, theories may be revised and even change; however, they are not tenuous or speculative.

A scientific hypothesis is an inferred explanation of an observation or research finding; while more exploratory in nature than a theory, it is based on existing scientific knowledge.

A scientific law is an expression of a mathematical or descriptive relationship observed in nature.

Imagine yourself shopping in a grocery store with a good friend who happens to be a chemist. Struggling to choose between the many different types of tomatoes in front of you, you pick one up, turn to your friend, and ask her if she thinks the tomato is organic . Your friend simply chuckles and replies, "Of course it's organic!" without even looking at how the fruit was grown. Why the amused reaction? Your friend is highlighting a simple difference in vocabulary. To a chemist, the term organic refers to any compound in which hydrogen is bonded to carbon. Tomatoes (like all plants) are abundant in organic compounds – thus your friend's laughter. In modern agriculture, however, organic has come to mean food items grown or raised without the use of chemical fertilizers, pesticides, or other additives.

So who is correct? You both are. Both uses of the word are correct, though they mean different things in different contexts. There are, of course, lots of words that have more than one meaning (like bat , for example), but multiple meanings can be especially confusing when two meanings convey very different ideas and are specific to one field of study.

• Scientific theories

The term theory also has two meanings, and this double meaning often leads to confusion. In common language, the term theory generally refers to speculation or a hunch or guess. You might have a theory about why your favorite sports team isn't playing well, or who ate the last cookie from the cookie jar. But these theories do not fit the scientific use of the term. In science, a theory is a well-substantiated and comprehensive set of ideas that explains a phenomenon in nature. A scientific theory is based on large amounts of data and observations that have been collected over time. Scientific theories can be tested and refined by additional research , and they allow scientists to make predictions. Though you may be correct in your hunch, your cookie jar conjecture doesn't fit this more rigorous definition.

All scientific disciplines have well-established, fundamental theories . For example, atomic theory describes the nature of matter and is supported by multiple lines of evidence from the way substances behave and react in the world around us (see our series on Atomic Theory ). Plate tectonic theory describes the large scale movement of the outer layer of the Earth and is supported by evidence from studies about earthquakes , magnetic properties of the rocks that make up the seafloor , and the distribution of volcanoes on Earth (see our series on Plate Tectonic Theory ). The theory of evolution by natural selection , which describes the mechanism by which inherited traits that affect survivability or reproductive success can cause changes in living organisms over generations , is supported by extensive studies of DNA , fossils , and other types of scientific evidence (see our Charles Darwin series for more information). Each of these major theories guides and informs modern research in those fields, integrating a broad, comprehensive set of ideas.

So how are these fundamental theories developed, and why are they considered so well supported? Let's take a closer look at some of the data and research supporting the theory of natural selection to better see how a theory develops.

Comprehension Checkpoint

• The development of a scientific theory: Evolution and natural selection

The theory of evolution by natural selection is sometimes maligned as Charles Darwin 's speculation on the origin of modern life forms. However, evolutionary theory is not speculation. While Darwin is rightly credited with first articulating the theory of natural selection, his ideas built on more than a century of scientific research that came before him, and are supported by over a century and a half of research since.

• The Fixity Notion: Linnaeus

Figure 1: Cover of the 1760 edition of Systema Naturae .

Research about the origins and diversity of life proliferated in the 18th and 19th centuries. Carolus Linnaeus , a Swedish botanist and the father of modern taxonomy (see our module Taxonomy I for more information), was a devout Christian who believed in the concept of Fixity of Species , an idea based on the biblical story of creation. The Fixity of Species concept said that each species is based on an ideal form that has not changed over time. In the early stages of his career, Linnaeus traveled extensively and collected data on the structural similarities and differences between different species of plants. Noting that some very different plants had similar structures, he began to piece together his landmark work, Systema Naturae, in 1735 (Figure 1). In Systema , Linnaeus classified organisms into related groups based on similarities in their physical features. He developed a hierarchical classification system , even drawing relationships between seemingly disparate species (for example, humans, orangutans, and chimpanzees) based on the physical similarities that he observed between these organisms. Linnaeus did not explicitly discuss change in organisms or propose a reason for his hierarchy, but by grouping organisms based on physical characteristics, he suggested that species are related, unintentionally challenging the Fixity notion that each species is created in a unique, ideal form.

• The age of Earth: Leclerc and Hutton

Also in the early 1700s, Georges-Louis Leclerc, a French naturalist, and James Hutton , a Scottish geologist, began to develop new ideas about the age of the Earth. At the time, many people thought of the Earth as 6,000 years old, based on a strict interpretation of the events detailed in the Christian Old Testament by the influential Scottish Archbishop Ussher. By observing other planets and comets in the solar system , Leclerc hypothesized that Earth began as a hot, fiery ball of molten rock, mostly consisting of iron. Using the cooling rate of iron, Leclerc calculated that Earth must therefore be at least 70,000 years old in order to have reached its present temperature.

Hutton approached the same topic from a different perspective, gathering observations of the relationships between different rock formations and the rates of modern geological processes near his home in Scotland. He recognized that the relatively slow processes of erosion and sedimentation could not create all of the exposed rock layers in only a few thousand years (see our module The Rock Cycle ). Based on his extensive collection of data (just one of his many publications ran to 2,138 pages), Hutton suggested that the Earth was far older than human history – hundreds of millions of years old.

While we now know that both Leclerc and Hutton significantly underestimated the age of the Earth (by about 4 billion years), their work shattered long-held beliefs and opened a window into research on how life can change over these very long timescales.

• Fossil studies lead to the development of a theory of evolution: Cuvier

Figure 2: Illustration of an Indian elephant jaw and a mammoth jaw from Cuvier's 1796 paper.

With the age of Earth now extended by Leclerc and Hutton, more researchers began to turn their attention to studying past life. Fossils are the main way to study past life forms, and several key studies on fossils helped in the development of a theory of evolution . In 1795, Georges Cuvier began to work at the National Museum in Paris as a naturalist and anatomist. Through his work, Cuvier became interested in fossils found near Paris, which some claimed were the remains of the elephants that Hannibal rode over the Alps when he invaded Rome in 218 BCE . In studying both the fossils and living species , Cuvier documented different patterns in the dental structure and number of teeth between the fossils and modern elephants (Figure 2) (Horner, 1843). Based on these data , Cuvier hypothesized that the fossil remains were not left by Hannibal, but were from a distinct species of animal that once roamed through Europe and had gone extinct thousands of years earlier: the mammoth. The concept of species extinction had been discussed by a few individuals before Cuvier, but it was in direct opposition to the Fixity of Species concept – if every organism were based on a perfectly adapted, ideal form, how could any cease to exist? That would suggest it was no longer ideal.

While his work provided critical evidence of extinction , a key component of evolution , Cuvier was highly critical of the idea that species could change over time. As a result of his extensive studies of animal anatomy, Cuvier had developed a holistic view of organisms , stating that the

number, direction, and shape of the bones that compose each part of an animal's body are always in a necessary relation to all the other parts, in such a way that ... one can infer the whole from any one of them ...

In other words, Cuvier viewed each part of an organism as a unique, essential component of the whole organism. If one part were to change, he believed, the organism could not survive. His skepticism about the ability of organisms to change led him to criticize the whole idea of evolution , and his prominence in France as a scientist played a large role in discouraging the acceptance of the idea in the scientific community.

• Studies of invertebrates support a theory of change in species: Lamarck

Jean Baptiste Lamarck, a contemporary of Cuvier's at the National Museum in Paris, studied invertebrates like insects and worms. As Lamarck worked through the museum's large collection of invertebrates, he was impressed by the number and variety of organisms . He became convinced that organisms could, in fact, change through time, stating that

... time and favorable conditions are the two principal means which nature has employed in giving existence to all her productions. We know that for her time has no limit, and that consequently she always has it at her disposal.

This was a radical departure from both the fixity concept and Cuvier's ideas, and it built on the long timescale that geologists had recently established. Lamarck proposed that changes that occurred during an organism 's lifetime could be passed on to their offspring, suggesting, for example, that a body builder's muscles would be inherited by their children.

As it turned out, the mechanism by which Lamarck proposed that organisms change over time was wrong, and he is now often referred to disparagingly for his "inheritance of acquired characteristics" idea. Yet despite the fact that some of his ideas were discredited, Lamarck established a support for evolutionary theory that others would build on and improve.

• Rock layers as evidence for evolution: Smith

In the early 1800s, a British geologist and canal surveyor named William Smith added another component to the accumulating evidence for evolution . Smith observed that rock layers exposed in different parts of England bore similarities to one another: These layers (or strata) were arranged in a predictable order, and each layer contained distinct groups of fossils . From this series of observations , he developed a hypothesis that specific groups of animals followed one another in a definite sequence through Earth's history, and this sequence could be seen in the rock layers. Smith's hypothesis was based on his knowledge of geological principles , including the Law of Superposition.

The Law of Superposition states that sediments are deposited in a time sequence, with the oldest sediments deposited first, or at the bottom, and newer layers deposited on top. The concept was first expressed by the Persian scientist Avicenna in the 11th century, but was popularized by the Danish scientist Nicolas Steno in the 17th century. Note that the law does not state how sediments are deposited; it simply describes the relationship between the ages of deposited sediments.

Figure 3: Engraving from William Smith's 1815 monograph on identifying strata by fossils.

Smith backed up his hypothesis with extensive drawings of fossils uncovered during his research (Figure 3), thus allowing other scientists to confirm or dispute his findings. His hypothesis has, in fact, been confirmed by many other scientists and has come to be referred to as the Law of Faunal Succession. His work was critical to the formation of evolutionary theory as it not only confirmed Cuvier's work that organisms have gone extinct , but it also showed that the appearance of life does not date to the birth of the planet. Instead, the fossil record preserves a timeline of the appearance and disappearance of different organisms in the past, and in doing so offers evidence for change in organisms over time.

• The theory of evolution by natural selection: Darwin and Wallace

It was into this world that Charles Darwin entered: Linnaeus had developed a taxonomy of organisms based on their physical relationships, Leclerc and Hutton demonstrated that there was sufficient time in Earth's history for organisms to change, Cuvier showed that species of organisms have gone extinct , Lamarck proposed that organisms change over time, and Smith established a timeline of the appearance and disappearance of different organisms in the geological record .

Figure 4: Title page of the 1859 Murray edition of the Origin of Species by Charles Darwin.

Charles Darwin collected data during his work as a naturalist on the HMS Beagle starting in 1831. He took extensive notes on the geology of the places he visited; he made a major find of fossils of extinct animals in Patagonia and identified an extinct giant ground sloth named Megatherium . He experienced an earthquake in Chile that stranded beds of living mussels above water, where they would be preserved for years to come.

Perhaps most famously, he conducted extensive studies of animals on the Galápagos Islands, noting subtle differences in species of mockingbird, tortoise, and finch that were isolated on different islands with different environmental conditions. These subtle differences made the animals highly adapted to their environments .

This broad spectrum of data led Darwin to propose an idea about how organisms change "by means of natural selection" (Figure 4). But this idea was not based only on his work, it was also based on the accumulation of evidence and ideas of many others before him. Because his proposal encompassed and explained many different lines of evidence and previous work, they formed the basis of a new and robust scientific theory regarding change in organisms – the theory of evolution by natural selection .

Darwin's ideas were grounded in evidence and data so compelling that if he had not conceived them, someone else would have. In fact, someone else did. Between 1858 and 1859, Alfred Russel Wallace , a British naturalist, wrote a series of letters to Darwin that independently proposed natural selection as the means for evolutionary change. The letters were presented to the Linnean Society of London, a prominent scientific society at the time (see our module on Scientific Institutions and Societies ). This long chain of research highlights that theories are not just the work of one individual. At the same time, however, it often takes the insight and creativity of individuals to put together all of the pieces and propose a new theory . Both Darwin and Wallace were experienced naturalists who were familiar with the work of others. While all of the work leading up to 1830 contributed to the theory of evolution , Darwin's and Wallace's theory changed the way that future research was focused by presenting a comprehensive, well-substantiated set of ideas, thus becoming a fundamental theory of biological research.

• Expanding, testing, and refining scientific theories
• Genetics and evolution: Mendel and Dobzhansky

Since Darwin and Wallace first published their ideas, extensive research has tested and expanded the theory of evolution by natural selection . Darwin had no concept of genes or DNA or the mechanism by which characteristics were inherited within a species . A contemporary of Darwin's, the Austrian monk Gregor Mendel , first presented his own landmark study, Experiments in Plant Hybridization, in 1865 in which he provided the basic patterns of genetic inheritance , describing which characteristics (and evolutionary changes) can be passed on in organisms (see our Genetics I module for more information). Still, it wasn't until much later that a "gene" was defined as the heritable unit.

In 1937, the Ukrainian born geneticist Theodosius Dobzhansky published Genetics and the Origin of Species , a seminal work in which he described genes themselves and demonstrated that it is through mutations in genes that change occurs. The work defined evolution as "a change in the frequency of an allele within a gene pool" ( Dobzhansky, 1982 ). These studies and others in the field of genetics have added to Darwin's work, expanding the scope of the theory .

• Evolution under a microscope: Lenski

More recently, Dr. Richard Lenski, a scientist at Michigan State University, isolated a single Escherichia coli bacterium in 1989 as the first step of the longest running experimental test of evolutionary theory to date – a true test meant to replicate evolution and natural selection in the lab.

After the single microbe had multiplied, Lenski isolated the offspring into 12 different strains , each in their own glucose-supplied culture, predicting that the genetic make-up of each strain would change over time to become more adapted to their specific culture as predicted by evolutionary theory . These 12 lines have been nurtured for over 40,000 bacterial generations (luckily bacterial generations are much shorter than human generations) and exposed to different selective pressures such as heat , cold, antibiotics, and infection with other microorganisms. Lenski and colleagues have studied dozens of aspects of evolutionary theory with these genetically isolated populations . In 1999, they published a paper that demonstrated that random genetic mutations were common within the populations and highly diverse across different individual bacteria . However, "pivotal" mutations that are associated with beneficial changes in the group are shared by all descendants in a population and are much rarer than random mutations, as predicted by the theory of evolution by natural selection (Papadopoulos et al., 1999).

• Punctuated equilibrium: Gould and Eldredge

While established scientific theories like evolution have a wealth of research and evidence supporting them, this does not mean that they cannot be refined as new information or new perspectives on existing data become available. For example, in 1972, biologist Stephen Jay Gould and paleontologist Niles Eldredge took a fresh look at the existing data regarding the timing by which evolutionary change takes place. Gould and Eldredge did not set out to challenge the theory of evolution; rather they used it as a guiding principle and asked more specific questions to add detail and nuance to the theory. This is true of all theories in science: they provide a framework for additional research. At the time, many biologists viewed evolution as occurring gradually, causing small incremental changes in organisms at a relatively steady rate. The idea is referred to as phyletic gradualism , and is rooted in the geological concept of uniformitarianism . After reexamining the available data, Gould and Eldredge came to a different explanation, suggesting that evolution consists of long periods of stability that are punctuated by occasional instances of dramatic change – a process they called punctuated equilibrium .

Like Darwin before them, their proposal is rooted in evidence and research on evolutionary change, and has been supported by multiple lines of evidence. In fact, punctuated equilibrium is now considered its own theory in evolutionary biology. Punctuated equilibrium is not as broad of a theory as natural selection . In science, some theories are broad and overarching of many concepts, such as the theory of evolution by natural selection; others focus on concepts at a smaller, or more targeted, scale such as punctuated equilibrium. And punctuated equilibrium does not challenge or weaken the concept of natural selection; rather, it represents a change in our understanding of the timing by which change occurs in organisms , and a theory within a theory. The theory of evolution by natural selection now includes both gradualism and punctuated equilibrium to describe the rate at which change proceeds.

• Hypotheses and laws: Other scientific concepts

One of the challenges in understanding scientific terms like theory is that there is not a precise definition even within the scientific community. Some scientists debate over whether certain proposals merit designation as a hypothesis or theory , and others mistakenly use the terms interchangeably. But there are differences in these terms. A hypothesis is a proposed explanation for an observable phenomenon. Hypotheses , just like theories , are based on observations from research . For example, LeClerc did not hypothesize that Earth had cooled from a molten ball of iron as a random guess; rather, he developed this hypothesis based on his observations of information from meteorites.

A scientist often proposes a hypothesis before research confirms it as a way of predicting the outcome of study to help better define the parameters of the research. LeClerc's hypothesis allowed him to use known parameters (the cooling rate of iron) to do additional work. A key component of a formal scientific hypothesis is that it is testable and falsifiable. For example, when Richard Lenski first isolated his 12 strains of bacteria , he likely hypothesized that random mutations would cause differences to appear within a period of time in the different strains of bacteria. But when a hypothesis is generated in science, a scientist will also make an alternative hypothesis , an explanation that explains a study if the data do not support the original hypothesis. If the different strains of bacteria in Lenski's work did not diverge over the indicated period of time, perhaps the rate of mutation was slower than first thought.

So you might ask, if theories are so well supported, do they eventually become laws? The answer is no – not because they aren't well-supported, but because theories and laws are two very different things. Laws describe phenomena, often mathematically. Theories, however, explain phenomena. For example, in 1687 Isaac Newton proposed a Theory of Gravitation, describing gravity as a force of attraction between two objects. As part of this theory, Newton developed a Law of Universal Gravitation that explains how this force operates. This law states that the force of gravity between two objects is inversely proportional to the square of the distance between those objects. Newton 's Law does not explain why this is true, but it describes how gravity functions (see our Gravity: Newtonian Relationships module for more detail). In 1916, Albert Einstein developed his theory of general relativity to explain the mechanism by which gravity has its effect. Einstein's work challenges Newton's theory, and has been found after extensive testing and research to more accurately describe the phenomenon of gravity. While Einstein's work has replaced Newton's as the dominant explanation of gravity in modern science, Newton's Law of Universal Gravitation is still used as it reasonably (and more simply) describes the force of gravity under many conditions. Similarly, the Law of Faunal Succession developed by William Smith does not explain why organisms follow each other in distinct, predictable ways in the rock layers, but it accurately describes the phenomenon.

Theories, hypotheses , and laws drive scientific progress

Theories, hypotheses , and laws are not simply important components of science, they drive scientific progress. For example, evolutionary biology now stands as a distinct field of science that focuses on the origins and descent of species . Geologists now rely on plate tectonics as a conceptual model and guiding theory when they are studying processes at work in Earth's crust . And physicists refer to atomic theory when they are predicting the existence of subatomic particles yet to be discovered. This does not mean that science is "finished," or that all of the important theories have been discovered already. Like evolution , progress in science happens both gradually and in short, dramatic bursts. Both types of progress are critical for creating a robust knowledge base with data as the foundation and scientific theories giving structure to that knowledge.

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• Theories, hypotheses, and laws drive scientific progress

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10 Scientific Laws and Theories You Really Should Know

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Scientists have many tools available to them when attempting to describe how nature and the universe at large work. Often they reach for laws and theories first. What's the difference? A scientific law can often be reduced to a mathematical statement, such as E = mc²; it's a specific statement based on empirical data, and its truth is generally confined to a certain set of conditions. For example, in the case of E = mc², c refers to the speed of light in a vacuum.

A scientific theory often seeks to synthesize a body of evidence or observations of particular phenomena. It's generally — though by no means always — a grander, testable statement about how nature operates. You can't necessarily reduce a scientific theory to a pithy statement or equation, but it does represent something fundamental about how nature works.

Both laws and theories depend on basic elements of the scientific method, such as generating a hypothesis , testing that premise, finding (or not finding) empirical evidence and coming up with conclusions. Eventually, other scientists must be able to replicate the results if the experiment is destined to become the basis for a widely accepted law or theory.

In this article, we'll look at 10 scientific laws and theories that you might want to brush up on, even if you don't find yourself, say, operating a scanning electron microscope all that frequently. We'll start off with a bang and move on to the basic laws of the universe, before hitting evolution . Finally, we'll tackle some headier material, delving into the realm of quantum physics.

• Big Bang Theory
• Hubble's Law of Cosmic Expansion
• Kepler's Laws of Planetary Motion
• Universal Law of Gravitation
• Newton's Laws of Motion
• Laws of Thermodynamics
• Archimedes' Buoyancy Principle
• Evolution and Natural Selection
• Theory of General Relativity
• Heisenberg's Uncertainty Principle

10: Big Bang Theory

If you're going to know one scientific theory, make it the one that explains how the universe arrived at its present state. Based on research performed by Edwin Hubble, Georges Lemaitre and Albert Einstein, among others, the big bang theory postulates that the universe began almost 14 billion years ago with a massive expansion event. At the time, the universe was confined to a single point, encompassing all of the universe's matter. That original movement continues today, as the universe keeps expanding outward.

The theory of the big bang gained widespread support in the scientific community after Arno Penzias and Robert Wilson discovered cosmic microwave background radiation in 1965. Using radio telescopes, the two astronomers detected cosmic noise, or static, that didn't dissipate over time. Collaborating with Princeton researcher Robert Dicke, the pair confirmed Dicke's hypothesis that the original big bang left behind low-level radiation detectable throughout the universe.

9: Hubble's Law of Cosmic Expansion

Let's stick with Edwin Hubble for a second. While the 1920s roared past and the Great Depression limped by, Hubble was performing groundbreaking astronomical research. Hubble not only proved that there were other galaxies besides the Milky Way , he also discovered that these galaxies were zipping away from our own, a motion he called recession .

In order to quantify the velocity of this galactic movement, Hubble proposed Hubble's Law of Cosmic Expansion , aka Hubble's law, an equation that states: velocity = H × distance . Velocity represents the galaxy's recessional velocity; H is the Hubble constant, or parameter that indicates the rate at which the universe is expanding; and distance is the galaxy's distance from the one with which it's being compared.

Hubble's constant has been calculated at different values over time, but the current accepted value is 70 kilometers/second per megaparsec, the latter being a unit of distance in intergalactic space [source: White ]. For our purposes, that's not so important. What matters most is that Hubble's law provides a concise method for measuring a galaxy's velocity in relation to our own. And perhaps most significantly, the law established that the universe is made up of many galaxies, whose movements trace back to the big bang.

8: Kepler's Laws of Planetary Motion

For centuries, scientists battled with one another and with religious leaders about the planets' orbits, especially about whether they orbited our sun. In the 16th century, Copernicus put forth his controversial concept of a heliocentric solar system, in which the planets revolved around the sun — not Earth. But it would take Johannes Kepler, building on work performed by Tyco Brahe and others, to establish a clear scientific foundation for the planets' movements.

Kepler's three laws of planetary motion — formed in the early 17th century — describe how planets orbit the sun. The first law, sometimes called the law of orbits , states that planets orbit the sun elliptically. The second law, the law of areas , states that a line connecting a planet to the sun covers an equal area over equal periods of time. In other words, if you're measuring the area created by drawing a line from Earth to the sun and tracking Earth's movement over 30 days, the area will be the same no matter where Earth is in its orbit when measurements begin.

The third one, the law of periods , allows us to establish a clear relationship between a planet's orbital period and its distance from the sun. Thanks to this law, we know that a planet relatively close to the sun, like Venus, has a far briefer orbital period than a distant planet, such as Neptune.

7: Universal Law of Gravitation

We may take it for granted now, but more than 300 years ago Sir Isaac Newton proposed a revolutionary idea: that any two objects, no matter their mass, exert gravitational force toward one another. This law is represented by an equation that many high schoolers encounter in physics class. It goes as follows:

F = G × [(m 1 m 2 )/r 2 ]

F is the gravitational force between the two objects, measured in Newtons. M 1 and m 2 are the masses of the two objects, while r is the distance between them. G is the gravitational constant , a number currently calculated to be 6.672 × 10 -11 N m 2 kg -2 [source: Weisstein ].

The benefit of the universal law of gravitation is that it allows us to calculate the gravitational pull between any two objects. This ability is especially useful when scientists are, say, planning to put a satellite in orbit or charting the course of the moon .

6: Newton's Laws of Motion

As long as we're talking about one of the greatest scientists who ever lived, let's move on to Newton's other famous laws. His three laws of motion form an essential component of modern physics. And like many scientific laws, they're rather elegant in their simplicity.

The first of the three laws states an object in motion stays in motion unless acted upon by an outside force. For a ball rolling across the floor, that outside force could be the friction between the ball and the floor, or it could be the toddler that kicks the ball in another direction.

The second law establishes a connection between an object's mass ( m ) and its acceleration ( a ), in the form of the equation F = m × a . F represents force, measured in Newtons. It's also a vector, meaning it has a directional component. Owing to its acceleration, that ball rolling across the floor has a particular vector , a direction in which it's traveling, and it's accounted for in calculating its force.

The third law is rather pithy and should be familiar to you: For every action there is an equal and opposite reaction. That is, for every force applied to an object or surface, that object pushes back with equal force.

5: Laws of Thermodynamics

The British physicist and novelist C.P. Snow once said that a nonscientist who didn't know the second law of thermodynamics was like a scientist who had never read Shakespeare [source: Lambert]. Snow's now-famous statement was meant to emphasize both the importance of thermodynamics and the necessity for nonscientists to learn about it.

Thermodynamics is the study of how energy works in a system, whether it's an engine or Earth's core. It can be reduced to several basic laws, which Snow cleverly summed up as follows [source: Physics Planet]:

• You can't win.
• You can't break even.
• You can't quit the game.

Let's unpack these a bit. By saying you can't win, Snow meant that since matter and energy are conserved, you can't get one without giving up some of the other (i.e., E=mc²). It also means that for an engine to produce work, you have to supply heat, although in anything other than a perfectly closed system, some heat is inevitably lost to the outside world, which then leads to the second law.

The second statement — you can't break even — means that due to ever-increasing entropy , you can't return to the same energy state. Energy concentrated in one place will always flow to places of lower concentration.

Finally, the third law — you can't quit the game — refers to absolute zero, the lowest theoretical temperature possible, measured at zero Kelvin or (minus 273.15 degrees Celsius and minus 459.67 degrees Fahrenheit). When a system reaches absolute zero, molecules stop all movement, meaning that there is no kinetic energy, and entropy reaches its lowest possible value. But in the real world, even in the recesses of space, reaching absolutely zero is impossible — you can only get very close to it.

4: Archimedes' Buoyancy Principle

After he discovered his principle of buoyancy, the ancient Greek scholar Archimedes allegedly yelled out "Eureka!" and ran naked through the city of Syracuse. The discovery was that important. The story goes that Archimedes made his great breakthrough when he noticed the water rise as he got into the tub [source: Quake ].

According to Archimedes' buoyancy principle , the force acting on, or buoying, a submerged or partially submerged object equals the weight of the liquid that the object displaces. This sort of principle has an immense range of applications and is essential to calculations of density, as well as designing submarines and other oceangoing vessels.

3: Evolution and Natural Selection

Now that we've established some of the fundamental concepts of how our universe began and how physics play out in our daily lives, let's turn our attention to the human form and how we got to be the way we are. According to most scientists, all life on Earth has a common ancestor. But in order to produce the immense amount of difference among all living organisms, certain ones had to evolve into distinct species.

In a basic sense, this differentiation occurred through evolution, through descent with modification [source: UCMP ]. Populations of organisms developed different traits, through mechanisms such as mutation. Those with traits that were more beneficial to survival such as, a frog whose brown coloring allows it to be camouflaged in a swamp, were naturally selected for survival; hence the term natural selection .

It's possible to expand upon both of these theories at greater length, but this is the basic, and groundbreaking, discovery that Darwin made in the 19th century: that evolution through natural selection accounts for the tremendous diversity of life on Earth.

2: Theory of General Relativity

Albert Einstein's theory of general relativity remains an important and essential discovery because it permanently altered how we look at the universe. Einstein's major breakthrough was to say that space and time are not absolutes and that gravity is not simply a force applied to an object or mass. Rather, the gravity associated with any mass curves the very space and time (often called space-time) around it.

To conceptualize this, imagine you're traveling across the Earth in a straight line, heading east, starting somewhere in the Northern Hemisphere. After a while, if someone were to pinpoint your position on a map, you'd actually be both east and far south of your original position. That's because Earth is curved. To travel directly east, you'd have to take into account the shape of Earth and angle yourself slightly north. (Think about the difference between a flat paper map and a spherical globe.)

Space is pretty much the same. For example, to the occupants of the shuttle orbiting Earth, it can look like they're traveling on a straight line through space. In reality, the space-time around them is being curved by Earth's gravity (as it would be with any large object with immense gravity such as a planet or a black hole), causing them to both move forward and to appear to orbit Earth.

Einstein's theory had tremendous implications for the future of astrophysics and cosmology. It explained a minor, unexpected anomaly in Mercury's orbit, showed how starlight bends and laid the theoretical foundations for black holes.

1: Heisenberg's Uncertainty Principle

Einstein's broader theory of relativity told us more about how the universe works and helped to lay the foundation for quantum physics, but it also introduced more confusion into theoretical science. In 1927, this sense that the universe's laws were, in some contexts, flexible, led to a groundbreaking discovery by the German scientist Werner Heisenberg.

In postulating his Uncertainty Principle , Heisenberg realized that it was impossible to simultaneously know, with a high level of precision, two properties of a particle. In other words, you can know the position of an electron with a high degree of certainty, but not its momentum and vice versa.

Niels Bohr later made a discovery that helps to explain Heisenberg's principle. Bohr found that an electron has the qualities of both a particle and a wave, a concept known as wave-particle duality , which has become a cornerstone of quantum physics. So when we measure an electron's position, we are treating it as a particle at a specific point in space, with an uncertain wavelength. When we measure its momentum, we are treating it as a wave, meaning we can know the amplitude of its wavelength but not its location.

Keep reading for more science stuff you might like.

Scientific Theory FAQ

What is scientific theory, what is an example of scientific theory, is a scientific law more accurate than a scientific theory, what are the five scientific laws, lots more information, related articles.

• Gravitational Waves! Or the Chirps That Prove Einstein Was Right
• How Newton's Laws of Motion Work
• 10 Scientific Words You're Probably Using Wrong
• How the Scientific Method Works
• Ask an Astronomer. "The Theory of Relativity." Cornell University Astronomy Dept. March 21, 2008. (Jan. 5, 2011) http://curious.astro.cornell.edu/relativity.php
• Bragg, Melvyn. "The Second Law of Thermodynamics." BBC. Dec. 16, 2004. (Jan. 5, 2011) http://www.bbc.co.uk/programmes/p004y2bm
• Glenn Research Center. "First Law of Thermodynamics." NASA. July 11, 2008. (Jan. 5, 2011) http://www.grc.nasa.gov/WWW/K-12/airplane/thermo1.html
• Lambert, Frank L. "Shakespeare and Thermodynamics: Dam the Second Law!" Occidental College. 2008. (Jan. 5, 2011) http://shakespeare2ndlaw.oxy.edu/
• LaRocco, Chris and Blair Rothstein. "The Big Bang." University of Michigan. (Jan. 5, 2011) http://www.umich.edu/~gs265/bigbang.htm
• Lightman, Alan. "Relativity and the Cosmos." PBS Nova. June 2005. (Jan. 5, 2011) http://www.pbs.org/wgbh/nova/einstein/relativity/
• Matson, Ronald H. "Scientific Laws and Theories." Kennesaw State University. (Jan. 5, 2011) http://science.kennesaw.edu/~rmatson/3380theory.html
• Nave, C.R. "Hubble law and the expanding universe." Georgia State University. (Jan. 5, 2011) http://hyperphysics.phy-astr.gsu.edu/hbase/astro/hubble.html
• Nave, C.R. "Kepler's Laws." Georgia State University. (Jan. 5, 2011) http://hyperphysics.phy-astr.gsu.edu/hbase/kepler.html
• Nave, C.R. "The Uncertainty Principle." Georgia State University. (Jan. 5, 2011) http://hyperphysics.phy-astr.gsu.edu/hbase/uncer.html
• PBS. "Big bang theory is introduced." 1998. (Jan. 5, 2011) http://www.pbs.org/wgbh/aso/databank/entries/dp27bi.html
• PBS. "Heisenberg states the uncertainty principle." 1998. (Jan. 5, 2011) http://www.pbs.org/wgbh/aso/databank/entries/dp27un.html
• PBS. "Penzias and Wilson discover cosmic microwave radiation." 1998. (Jan. 5, 2011) http://www.pbs.org/wgbh/aso/databank/entries/dp65co.html
• Pidwirny, Michael. "Laws of Thermodynamics." Physical Geography. April 6, 2010. (Jan. 5, 2011) http://www.physicalgeography.net/fundamentals/6e.html
• Quake, Stephen. "Practically pure." The New York Times. Nov. 8, 2009. (Jan. 5, 2011) http://www.nytimes.com/2009/02/18/opinion/18iht-edquake.1.20274600.html
• Stern, David P. "Kepler's Three Laws of Planetary Motion." Phy6.org. March 21, 2005. (Jan. 5, 2011) http://www.phy6.org/stargaze/Kep3laws.htm
• Stern, David P. "Newton's theory of 'Universal Gravitation'." NASA. March 24, 2006. (Jan. 5, 2011) http://www-istp.gsfc.nasa.gov/stargaze/Sgravity.htm
• University of California Museum of Paleontology (UCMP). "Understanding Evolution: An introduction to evolution." (Jan. 5, 2011) http://evolution.berkeley.edu/evolibrary/article/0_0_0/evo_02
• University of California Museum of Paleontology (UCMP). "Understanding Evolution: Natural selection." (Jan. 5, 2011) http://evolution.berkeley.edu/evolibrary/article/evo_25
• University of Tennessee, Knoxville, Dept. of Physics & Astronomy. "Newton's Three Laws of Motion." (Jan. 5, 2011) http://csep10.phys.utk.edu/astr161/lect/history/newton3laws.html
• University of Tennessee, Knoxville, Dept. of Physics & Astronomy. "Sir Isaac Newton: The Universal Law of Gravitation." (Jan. 5, 2011) http://csep10.phys.utk.edu/astr161/lect/history/newtongrav.html
• Weisstein, Eric W. "Gravitational Constant." Wolfram Research. (Jan. 5, 2011) http://scienceworld.wolfram.com/physics/GravitationalConstant.html
• Weisstein, Eric W. "Kepler's Laws." Wolfram Research. (Jan. 5, 2011)http://scienceworld.wolfram.com/physics/KeplersLaws.html
• White, Martin. "The Hubble Expansion." University of California, Berkeley. (Jan. 5, 2011) http://astro.berkeley.edu/~mwhite/darkmatter/hubble.html

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Scientific Method: Step 3: HYPOTHESIS

• Step 1: QUESTION
• Step 2: RESEARCH
• Step 3: HYPOTHESIS
• Step 4: EXPERIMENT
• Step 5: DATA
• Step 6: CONCLUSION

Step 3: State your hypothesis

Now it's time to state your hypothesis . The hypothesis is an educated guess as to what will happen during your experiment. 

The hypothesis is often written using the words "IF" and "THEN." For example, " If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables . 

The hypothesis should relate back to your original question and must be testable .

A word about variables...

Your experiment will include variables to measure and to explain any cause and effect. Below you will find some useful links describing the different types of variables.

• "What are independent and dependent variables" NCES
• [VIDEO] Biology: Independent vs. Dependent Variables (Nucleus Medical Media) Video explaining independent and dependent variables, with examples.

Resource Links

• What is and How to Write a Good Hypothesis in Research? (Elsevier)
• Hypothesis brochure from Penn State/Berks

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Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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7 scientific terms you might be using wrong

Misused scientific terms

Pipeline of proposed research methodology.

Data preprocessing

Firstly, this subsection goes into the specifics of the disease dataset that was utilized, and then data preprocessing is performed in this research. Our dataset provides a comprehensive compilation of symptoms and patient profiles for a range of diseases. The mysteries of diseases can be unveiled with this disease symptom and patient profile dataset. The analytic results can show intricate relationship between patients and diseases. In other words, the proposed system can assist in the extracting the AR and development of predictive models for disease diagnosis and monitoring based on symptoms and patient characteristics. On this subject, we utilized an available online data set by the Kaggle Repository. In our study, we used a publicly available Kaggle dataset that does not contain personally identifiable information (PII). To handle the sensitive health data responsibly, we consider ensured data anonymization and maintained compliance with data privacy regulations, such as HIPAA and GDPR. By taking these measures, we aim to protect the privacy and confidentiality of the patient information, comply with relevant data protection regulations, and conduct the research in an ethical and transparent manner, prioritizing the rights and well-being of the study participants. The dataset offers a detailed examination of the intricate relationships between patients and diseases, comprising over 100 distinct medical conditions and featuring 3490 records. The dataset offers a treasure trove of information including fever, cough, fatigue, and breathing difficulty, intertwined with age, gender, blood pressure, and cholesterol levels revealing the fascinating connections between symptoms, demographics, and health indicators. We aim to explore the hidden patterns, and uncover unique symptom profiles. The dataset has 10 attributes, which are given in Table 1 .

A classifier can be ineffective in processing raw data in some cases due to features such as incompleteness, noise, and inconsistency 33 . Data preprocessing is necessary for preparing a dataset to improve prediction accuracy in data mining and ML. The data was preprocessed before analysis, which included which included label encoding, data transformation, and handling outliers. During data preprocessing, label encoding is conducted to transform the data into numerical format. Categorical variables, which include symptoms, gender, blood pressure, cholesterol level, and the outcome variable (disease), are often non-numeric and represent various categories or groups. During data preprocessing, label encoding is conducted to transform the data into numerical format. When categorical data is transformed into numerical data, predictive modeling and classification algorithms can effectively process and learn from the data. This transformation avoids misleading orderings, and enables the algorithms to create more accurate models, better generalize to new data, and ultimately improve model performance 34 . On the other hand, In JMP software, to perform AR mining, the data needs to be in list format, and then it should be transformed to nominal format type. This process allows the software to analyze transactional data and identify items that have an affinity for each other, a technique frequently used in market basket analysis.

One of the main issues in ML is dealing with outliers. An outlier is a data point that deviates from the typical behavior exhibited by other data points. The presence of outliers can impact the performance of AI-based forecasting methods and the discovery of diseases symptoms. Therefore, ensuring that the dataset is free of outliers is a critical task for achieving superior prediction results.

Note that the limitations of the dataset and acknowledge any biases or incompleteness that may affect the interpretation and application of the findings. These limitations include sample size and representation, data collection methodology, missing or incomplete data, geographical and demographic limitations, and temporal limitations. To minimize the influence of these constraints and improve the applicability of the outcomes, it is essential to carefully interpret the findings, and recognize the potential limitations and the importance of thoughtful consideration when extrapolating the results to particular patient groups or clinical situations.

Applied unsupervised ML method

AR learning in unsupervised ML algorithms is a valuable method for uncovering interesting connections among features in a dataset. The Apriori, Eclat, and FP-growth algorithms are widely used for AR learning and are instrumental in identifying patterns and associations in large datasets, offering valuable insights for various applications such as market basket analysis, customer segmentation, and recommendation systems. These algorithms play a crucial role in fields like retail, healthcare, and finance, where they help in understanding customer behavior, optimizing product offerings, and improving business strategies. The Apriori algorithm, for instance, is essential for data scientists and businesses seeking to extract meaningful patterns and associations from their data. We used the Apriori algorithm, which has a computational complexity of O(nlog n), where n is the number of data points. The time complexity is O(nlog n) for training and O(1) for prediction. The space complexity is O(n) for storing the model coefficients. Extracted AR are valuable for predicting class values in early-stage diseases. However, different criteria can be used to measure the strength of these rules. Some of these criteria are described below:

Support: This metric indicates how often a given rule appears in the database being mined.

Confidence: This metric refers to the number of times a given rule turns out to be true in practice.

Lift: This metric is utilized to compare the confidence of a rule with the expected confidence, or how many times an if-then statement is expected to be found true.

These metrics help assess the effectiveness of AR in predicting class values for early-stage diseases.

Applied supervised ML method

By harnessing the power of basic health indicators, we can improve the understanding of diseases and their progression, ultimately leading to better patient care and more effective interventions. Predictive modeling in supervised ML algorithms aims to extend a model that can accurately predict the value of a target variable based on one or more input variable. In this context, we will briefly discuss several popular supervised ML algorithms, including SR, SVM, BF, BT, and NB methods.

Sequential Regression (SR) is a statistical method utilized for feature selection within predictive modeling. It involves a systematic approach to identifying the optimal subset of predictors that exhibit the strongest correlation with the target variable. Through an iterative process, predictors are added or removed from the model based on their statistical significance and impact on the model’s overall performance. This iterative refinement continues until a feature set that maximizes model performance is determined. However, the computational demands of SR can be substantial, particularly with large datasets. The selection of the best feature subset entails evaluating numerous feature combinations, leading to potential time constraints. Moreover, as the dataset’s feature count grows, the computational complexity escalates, rendering SR less feasible for certain scenarios.

SVM are a supervised ML method that can be employed for both classification and regression tasks. SVM operates by identifying a hyperplane that best separates the data points, effectively finding a decision boundary that distinguishes the classes with the largest margin. In this context, SVM algorithms are used to create models that can make predictions based on known relationships between input and target variables, such as in classification problems, or continuous predictions in regression tasks. In the context of predictive modeling, SVM can be used to find a function that best predicts the value of the target variable using the input features. The computational complexity of SVMs is O(n 2 ), where n is the number of data points. The time complexity is O(n 2 ) for training and O(1) for prediction. The space complexity is O(n) for storing the model coefficients.

BF is a term that combines bootstrap aggregation (bagging) and RF. Bagging is a method that aims to enhance the accuracy and robustness of ML models by training multiple models on various subsets of the training data and then combining their predictions. RF is an ensemble learning method that creates multiple DT during training and outputs the class that is the mode of the classes of the individual trees. This method is used in supervised ML for both classification and regression tasks Therefore, BF refer to an ensemble learning method that combines the principles of bagging and RF. The computational complexity of BF is O(nlog n), where n is the number of data points. The time complexity is O(nlog n) for training and O(1) for prediction. The space complexity is O(n) for storing the model coefficients.

BT, such as Gradient Boosting are ensemble learning methods that combine the predictions of multiple weak learners to create a strong learner. These algorithms work by iteratively training and combining the predictions of weak learners, such as DT or linear regression models, to improve the overall accuracy and reduce overfitting. In the context of predictive modeling, BT can be applied to predict the value of a target variable using the input features. The computational complexity of BT is O(nlog n), where n is the number of data points. The time complexity is O(nlog n) for training and O(1) for prediction. The space complexity is O(n) for storing the model coefficients.

NB is a ML approach that combines neural networks and boosting algorithms to enhance prediction accuracy. Boosting is an ensemble learning method that merges weak learners to create a strong learner, reducing training errors. In contrast, neural networks are ML algorithms capable of discerning intricate patterns in data. NB integrates these techniques by training a neural network on a subset of the training data and then using boosting to combine multiple neural networks, creating a more precise model. The process involves iteratively training a neural network on a subset of the training data and then adding the network to the ensemble. The weights of the neural network are adjusted to minimize the error of the ensemble. NB has demonstrated effectiveness in various applications, such as image classification, speech recognition, and natural language processing. However, NB has limitations, including the potential for overfitting and the requirement for large amounts of training data. Despite these limitations, NB is a potent ML technique that can enhance prediction accuracy across a variety of applications. The computational complexity of NB methods is O(n 2 ), where n is the number of data points. The time complexity is O(n 2 ) for training and O(1) for prediction. The space complexity is O(n) for storing the model coefficients.

To enhance the description of the proposed method, we have outlined the key steps of the implementation of our methodology using JMP software.

Key steps for the proposed methodology.

The implementation of the methodology is available in the following GitHub repository: https://github.com/fsogandi/disease-symptoms.git

In this section, we analyze data to investigate disease symptoms using AR and predictive modeling.

Data preparation

As shown in Table 1 , blood pressure and cholesterol level characteristics are nominal data type. Hence, we use the transformation method and encoding to have Binary variables that are then treated as numeric. On the other hand, in JMP software, to perform AR mining, the data needs to be in list format, and then it should be transformed to nominal format type. In this respect, we treated each patient as a single transaction. Then, we divided the dataset into three groups based on the patient’s age to transform a list format including: young adult, middle-age adults, and older adults. We initially applied AR mining to symptom data and identified symptom rules. Additionally, to identify and mange outliers, we apply the KNN (K = 8), robust principal component analysis (with lambda = 0.107 and outlier threshold = 2), T 2 , Mahalanobis, and Jackknife distances methods. Generally, results show that the rows of 1, 81, 122, 213 are outlier and should be excluded. Note that KNN identifies outliers based on distance to each observation nearest neighbors for theses rows as well as 39 rows that we ignore it. Figure  2 shows outliers using T 2 , Mahalanobis, and Jackknife distances for instance.

Outlier plot for the T 2 , Mahalanobis, and Jackknife distances methods.

After data preparation phase, we perform a descriptive statistical analysis to help more ML methods. Some of these investigations are provided here. In this regard, the dataset does not exhibit significant skewness, with only a few outliers present, and the gender distribution in the dataset is relatively balanced. Figure  3 shows that individuals have a higher likelihood of testing positive for diseases, in older age. Additionally, Fig.  4 shows fever is a main symptom of these diseases. This figure demonstrates many individuals, regardless of the type of experience (positive or negative), report coughing.

Plot of outcome results in terms of age factor.

Bar graph of some symptoms of the diseases.

The more analytics using pie chart shows the majority of the individuals in the study have high blood pressure and cholesterol. Additionally, out of 348 patients, 185 tested positive for a disease. Only 23 of the positive cases developed all symptoms. The average age of the patients is 46, with the majority being middle-aged. However, positive cases are proportionally higher in older adults. A violin plot indicates that older adults have high blood pressure, but older adult to middle-aged patients also exhibit high blood pressure. The most common symptoms were fatigue (139 cases), fever (109 cases), breathing difficulty, and cough (both seen in 88 cases). Females are more prone to the diseases than Males.

AR in unsupervised ML

We used an a Apriori method to extract lift matrix-based strong rules. Symptom transactions are part of the AR mining which aims to identify frequent item sets that meet a minimum threshold. To achieve this, we set the minimum confidence level to 1, ensuring that all generated rules have a 100% confidence level. Additionally, we establish a minimum support threshold above 0.01 and a lift greater than 4 for positively correlated rules. This means that the rules generated must have a support value greater than 1% and a lift value greater than 4, indicating a strong positive correlation between the antecedent and consequent items. Furthermore, we limit the maximum number of antecedents to 3 and the maximum rule size to 4, ensuring that the generated rules are concise and interpretable. To do so, we discover many significant AR for the data, and the top 20 symptom rules by highest lift values are given in Table 2 . Table 2 concentrates on the antecedents (diseases) associated with the consequents (symptoms) to predict asymptotes of diseases.

Table 2 shows diseases strongly linked to symptoms with a confidence of 100% (except for rule 18) and a lift greater than 1. A confidence level of 100% indicates a high degree of certainty. Lift measures the performance of an AR as a response enhancer. Lift values greater than 1 indicate interdependence between conditions and their outcomes, emphasizing positive relationships. Based on rule 2, if a patient had Chronic Obstructive Pulmonary Disease (COPD) (condition), then this patient had a higher confidence for breathing difficulty in older adults’ group (consequent). Specifically, Rule 1 suggests a positive association between Typhoid fever, high cholesterol, and fatigue, while rule 10 indicates that Hepatitis B increases the likelihood of coughing, fatigue, and high cholesterol. The results also demonstrate that demographic factors impact the relationships between symptom patterns and disease types. Additionally, the proposed model seeks to predict the potential disease of a patient based on their specific symptoms. In this regard, the 20 top rules are given in Table 3 .

The associations in Table 2 exhibit a high confidence and a lift greater than 1, indicating a positive links. For example, Table 3 shows 5 rules related to COPD with a 100% confidence level and a notably high lift. According to these rules, older adults experiencing breathing difficulty, fever, high blood pressure, high cholesterol, and fatigue have a 67% chance of having COPD. Similarly, the presence of high cholesterol, high blood pressure, and breathing difficulty in older adults may indicate a higher likelihood of Rheumatoid Arthritis. Additionally, the model aims to predict potential diseases based on specific symptoms, while also considering the influence of demographic factors on symptom-disease associations. Furthermore, the unsupervised algorithm can identify relationships between symptoms and various attributes, aiding in the discovery of symptom relationships. In this respect, 25 rules are extracted in Table 4 .

According to Table 4 , among all rules, fever was the most common consequent. To describe the extracted rules, we focus on one rule for instance. Based on rule 1, if a patient has breathing and coughing problems and high blood pressure there is a 100% confidence that he or she had a fever. Similarly, Rule 2 highlights that when a patient experience both fatigue and high cholesterol, they will also have a fever. Moreover, the last rule shows that male patient with breathing difficulty who are strongly associated with fever, with a confidence of 90%. In general, our analysis from Tables 2 – 4 shows that the older adults age group strongly correlated with diseases occurrence. Another analytic can be achieved from AR mining, gives in Table 5 in which the disease may be occurred in specified age are shown. To sum up, we provide some of the ages in this respect.

Moreover, we can identify diseases that have an affinity for each other using Singular Value Decomposition (SVD). Diseases that exhibit overlap, based on the SVD method, can be identified by leveraging the SVD technique. This approach decreases the dimensionality of the data, allowing for the grouping of similar diseases and the extraction of relevant information. Figure  5 and Table 6 show points or diseases that are close to each other.

Item SVD plots for the data set.

Predictive modeling in supervised ML

Now, we aim to develop a model that can accurately predict diseases using the disease symptoms and patient profile dataset. As aforementioned, this dataset contains valuable information on symptoms, demographics, and health indicators, which can be used to reveal fascinating connections and patterns. After examining the “Disease” column, we found that many unique diseases have only 1 to 5 samples, which is insufficient for a reliable disease prediction model. Predicting diseases with such limited information could lead to inaccurate results and misdiagnosis, which we want to avoid. Therefore, we will focus only on the diseases that have 10 or more samples to ensure the robustness of our model. This decision will reduce the number of cholesterol asses we are predicting down to 6, making our model more accurate. On the other hand, using checking for and handling missing values and identifying and removing duplicate entries we can ensure that our data is accurate, complete, and ready for further analysis or model building. After cleaning our data, we have focused on diseases with 10 or more samples. Understanding the balance of cholesterol asses is crucial as it can impact the performance of our ML model. To visualize this, we have utilized a pie chart in Figure 6 . This step is essential for ensuring that our model is trained on a well-balanced dataset, which can ultimately enhance its predictive accuracy and reliability.

Pie chart for initial diseases classification.

The pie chart shows that the classes are imbalanced, and we need to handle class imbalance. Before that, we need to process our categorical variables to perform a univariate analysis. This analysis will help us understand the distribution of our variables and their individual impact on disease prediction. We will start with the age variable, followed by other variables like symptoms, gender, blood pressure, and cholesterol level.

The univariate analysis of the age variable in Fig.  7 reveals that age is a valuable feature for predicting certain diseases. For instance, if the age is greater than 80, the disease is likely to be a stroke. However, the dataset has limited samples, especially for ages greater than 80, which could make predicting new values in this age range challenging. The analysis also shows that some diseases like Migraine and Hypertension are not present in ages between 20 and 30, suggesting that these conditions are more prevalent in older age groups. Hypertension and Osteoporosis appear more frequently as the age increases, indicating a potential correlation between these diseases and age. Also, cholesterol levels and blood pressure, significantly influence disease prediction. For example, High blood pressure is associated with the absence of stroke, which is crucial for stroke prediction. These observations emphasize the importance of these variables in predicting diseases. The next step is to examine how these variables correlate with each other, which can help identify patterns and potential multicollinearity, ultimately influencing the model’s performance.

Bar graph of age-diseases.

Figure  8 shows that none of the variables have a strong correlation with the “Disease” variable. The most correlated variables are “Age” and “Difficulty Breathing”, with scores of 1 and − 1, respectively. In situations where there are multiple variables with high correlation scores, ML can be a viable alternative for prediction tasks. However, it’s essential to consider that ML algorithms, typically require large amounts of data to perform optimally. In our case, we have only 79 data points, which is relatively small.

Correlation of each feature in the dataset using the heat map generated by JMP Pro 17 (version 17.2.1, available at https://www.jmp.com/ ).

For hyperparameter tuning, we used the Grid Search method in JMP. Grid search is a simple and effective method for finding the optimal combination of hyperparameters by systematically varying each hyperparameter over a range of values and evaluating the performance of the model at each combination. We used a grid search with 10 iterations to find the optimal combination of hyperparameters for each model. For example, for the SR, we used a grid search to optimize the following hyperparameters:

Stepwise selection: We used a grid search to optimize the stepwise selection method. We varied the number of features to include in the model from 1 to 10, and evaluated the performance of the model at each combination.

Lambda: We used a grid search to optimize the lambda value, which is a hyperparameter that controls the strength of the regularization term in the model. We varied the lambda value from 0.1 to 1.0, and evaluated the performance of the model at each combination.

For the SVMs, we used a grid search to optimize the following hyperparameters:

Kernel: We used a grid search to optimize the kernel function, which is a hyperparameter that controls the shape of the decision boundary in the model. We varied the kernel function between linear, polynomial, and radial basis functions, and evaluated the performance of the model at each combination.

Gamma: We used a grid search to optimize the gamma value, which controls the width of the kernel function. We varied the gamma value under (0.1–1) and evaluated the performance of the model at each combination.

For the BF model, we used a grid search to optimize the following hyperparameters:

Number of trees: We used a grid search to optimize the number of trees in the forest that controls the complexity of the model. We varied the them under (10–100), and assessed the performance of the model at each combination.

Max depth: We used a grid search to optimize the maximum depth of the trees, which is a hyperparameter that controls the complexity of the model. We varied the maximum depth from 5 to 10, and evaluated the performance of the model at each combination.

For the BT model, we used a grid search to optimize the following hyperparameters:

Number of iterations: We used a grid search to optimize the number of iterations in the boosting algorithm, which is a hyperparameter that controls the complexity of the model. We varied the number of iterations from 10 to 100, and evaluated the performance of the model at each combination.

Learning rate: We used a grid search to optimize the learning rate that controls the step size in the boosting algorithm. We varied it under (0.1–1) and evaluated the performance of the model at each combination.

For the NB methods, we used a grid search to optimize the following hyperparameters:

Number of hidden layers: We used a grid search to optimize the number of hidden layers in the neural network, which is a hyperparameter that controls the complexity of the model. We varied the number of hidden layers from 1 to 3, and evaluated the performance of the model at each combination.

Number of neurons: We used a grid search to optimize the number of neurons in each hidden layer, which is a hyperparameter that controls the complexity of the model. We varied the number of neurons from 10 to 100, and evaluated the performance of the model at each combination.

To conduct a fair comparison between different classifiers and identify the superior model with the best performance, we have considered and calculated several evaluation metrics that are well-suited for our specific case and dataset. The evaluation metrics we have included are:

This is a crucial measure for evaluating imbalanced multi-class classification problems. A comparative assessment of most common used ML classifiers is performed in Table 7 for analyzing and classifying diseases.

We used the confusion matrix to calculate different metrics, and the best results are marked in bold. As illustrated by Table 7 , SR method is the superior model leading to the best performance with the accuracy of 86.73% (95% CI 82.69–90.71) and the precision of 75.36%. Besides, the corresponding criteria of recall and F1- measure and (Matthews Correlation Coefficient) MCC are 77.87, 81.31, and 54.02%, respectively. Based on these metrics, the “SR” model consistently performs well across evaluation criteria. To avoid additional complexity and keep this table simple to read, we preferred to exclude the standard deviation of each result metrics.

Overall, researchers focused on specific diseases or conditions mentioned in the dataset can utilize it to explore relationships between symptoms, age, gender, and other variables. Also, healthcare technology companies can use the proposed method based on ML methods for developing healthcare diagnostic tools. It is worth mentioning that the model shows strong performance in predicting asthma cases but struggles to predict other conditions, suggesting its potential use in a one-vs-all approach for asthma diagnosis. Notably, the training data is imbalanced, with asthma being the most frequent class. To address this, data augmentation techniques such as rotation, scaling, or adding noise could be implemented to improve the model’s accuracy in predicting less frequent diseases.

The study aims to identify common patterns and general rules across various diseases using ML techniques. By analyzing a diverse dataset, the research uncovers connections between symptoms, demographics, and health indicators, providing valuable insights for developing predictive models and early warning systems applicable to multiple diseases. It is worth noting that the decision to generalize the study across various diseases is grounded in several key considerations, including identifying common patterns, improving early detection, enhancing understanding, and practical implications. While the generalized approach offers several advantages, it is important to acknowledge that the study may not capture disease-specific nuances or rare symptoms that are unique to particular diseases. Future research could focus on validating the identified patterns and rules in specific disease contexts or exploring the applicability of the findings to rare or understudied diseases. In conclusion, the decision to generalize the study across various diseases is justified by the potential benefits of identifying common patterns, improving early detection, enhancing understanding, and providing practical implications for healthcare professionals. However, the limitations of this approach should be considered, and further research is needed to validate and refine the findings in specific disease contexts.

To improve the model’s ability to adapt to new, emerging diseases or changes in symptom presentation, the following strategies can be implemented in our approach:

We can easily implement a system to continuously collect and integrate new patient data into the training dataset, including information on emerging diseases and changing symptom patterns. The models can then be retrained on a regular cadence (e.g., monthly, quarterly) to ensure they remain up-to-date and can adapt to evolving disease landscapes. Additionally, we can monitor model performance on a holdout test set to identify when retraining is necessary due to degradation in predictive accuracy. This will help ensure the models can adapt to new, emerging diseases and changing symptom presentations. As a future research direction, we recommend exploring the use of ensemble learning techniques. Specifically, we suggest investigating the application of various ensemble methods to further enhance the ability of the proposed models.

Statistical significance

Now, we use a statistical test to compare the proposed ML to ensure the statistical significance of the results and provide a robust comparison. Overall, the non-parametric tests are safer than parametric tests since they do not assume normal distributions or homogeneity of variance. In the case where multiple algorithms are to be compared, Friedman’s test is the most interesting non-parametric statistical test. In Friedman test, the blocks of data, are considered independent. The underlying variables in the data are typically numeric in nature. The goal of this test is to determine whether there are significant differences among the algorithms considered over given sets of data. Training/Test set is generated as random sample from the population. The Friedman rank test can determine if there are significant differences in variation, central tendency, or shape among at least one pair of the populations being compared. The test determines the ranks of the algorithms for each individual data set, i.e., the best performing algorithm receives the rank of 1, the second-best rank 2, etc.; in the case of ties average ranks are assigned. The Friedman test is performed in respect of average ranks, which use \(\chi_{F}^{2}\) . Consider \(r_{i}^{j}\) be the rank of the j th of k ML algorithms on i th of n data sets. The Friedman test compares the average ranks of algorithms, \(R_{j}^{{}} .\) The null hypothesis states that all algorithms perform equivalently. Under this hypothesis the Friedman statistics is as follows:

in which \(\chi_{F}^{2}\) is distributed with k -1 degrees of freedom, when n and k are large enough. We can understand with comparing the corresponding statistics and \(\chi_{F}^{2} (4)\) with α = 0.05, the null hypothesis is rejected. In this regard, average rankings of the ML algorithms over the data sets by the Friedman test are shown in Table 8 .

Feature importance and scoring

In the literature, two primary strategies for feature selection are Forward Selection (FS) and Backward Elimination (BE) for our classifier. FS starts by selecting the best single feature and then iteratively adds the feature that improves performance the most. Conversely, the BE begins with all considered features and repeatedly removes the feature that reduces performance the most. We conducted a series of experiments using fivefold cross-validation. The dataset was divided into 80% training cases and 20% test cases. In each fold, the training data was used to calculate the accuracy of a random forest classifier using different sets of features. The set of features that yielded the best accuracy was retained. The results are presented in Table 9 .

The features were ranked incrementally based on their importance, with the most important feature labeled as one, the next most important feature labeled as two, and so on. Features with the “ignored” tag were removed from the dataset.

In the Forward Selection (FS) and Backward Elimination (BE) methods, we observe that the “age” and “breathing difficulty” features are consistently ranked as the most important, indicating its significant contribution to the model. Furthermore, we note that the “fatigue” feature is ranked last in both FS and BE, suggesting its relatively low relevance. Additionally, the “Blood pressure” feature is either ignored or ranked last in both methods, implying its minimal impact on the model. This further validates the effectiveness of our algorithm in ranking features.

Deployment challenges

Successful deployment of the ML models developed in this study necessitates careful consideration of the challenges to ensure effective implementation and adoption in real-world healthcare settings. The integration of these models into existing healthcare systems can pose significant challenges. Healthcare organizations often have complex and diverse IT infrastructures, with various systems and platforms in place. Seamless integration of the ML models into these existing systems is crucial for ensuring efficient data flow, accurate predictions, and effective decision support. Key considerations for integration include data compatibility, security and privacy, and scalability. Additionally, effective clinician training and adoption are crucial. Clinicians may be hesitant to rely on automated decision support systems, especially if they lack understanding of how the models work or have concerns about their accuracy and reliability. To address these challenges, the following strategies can be employed:

• Comprehensive training: Providing comprehensive training to clinicians on the use and interpretation of the ML models, including their strengths, limitations, and appropriate applications.

• Transparency: Ensuring that the ML models are as transparent and explainable as possible, allowing clinicians to understand the reasoning behind the predictions and build trust in the system.

• Continuous feedback and improvement: Establishing mechanisms for clinicians to provide feedback on the performance and usability of the ML models, enabling continuous improvement and adaptation to user needs.

• Incentives and support: Providing incentives and support for clinicians to adopt and integrate the ML models into their daily workflows, such as through performance metrics or dedicated support staff.

Successful deployment of ML models in healthcare requires careful consideration of integration challenges with existing systems and effective clinician training and adoption strategies. By addressing these challenges, healthcare organizations can effectively leverage the power of ML to improve patient outcomes and enhance clinical decision-making.

Conclusion and future research

Early disease prediction significantly enhances healthcare quality and can avert serious health complications. This proactive approach is particularly crucial due to the rise of new disease variants and the increasing availability of healthcare data. This study proposed an AI-based disease detection system for predicting diseases. Our results show several important results that enhance our diagnosing. In this regard, firstly we conduct data processing including data transformation and outlier detection, and then many significant AR was extracted based on Apriori algorithm. Generally, our research shows strong correlations between different variables, the occurrence of the diseases and medical conditions. For example, our study found that individuals in the older adults age group, those experiencing symptoms such as high cholesterol coughing and breathing difficulty have a strong relationship with Rheumatoid Arthritis. Additionally, various classification methods were applied to determine the best performing classifier, of the models investigated, SR method significantly outperformed the others. The proposed method can be used for medical practitioners, doctors, clinical analysis, and epidemiological investigations related to different diseases. It also can aid in understanding the prevalence and patterns of symptoms among patients with specific medical conditions.

We acknowledge the limitations of their research, which was based on a provided dataset that may not fully represent the diversity of patient populations. We recognize the need for larger-scale studies to validate the generalizability of their findings to other settings and populations. The authors emphasize the importance of future research to confirm their findings and investigate underlying mechanisms in more detail.

Additionally, we suggest that future work could involve the use of other types of ARM methods, such as the Frequent Pattern Growth to discover patterns. Future studies could consider using multiple datasets to improve the robustness of the findings. Overall, exploring different approaches, including data augmentation, is crucial to enhance the model’s accuracy and enable more precise predictions across a wider range of conditions.

Data availability

The dataset used in this study is publicly available in the Kaggle repository https://www.kaggle.com/datasets/uom190346a/disease-symptoms-and-patient-profile-dataset .

Yan, H., Jiang, Y., Zheng, J., Peng, C. & Li, Q. A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Syst. Appl. 30 , 272–281. https://doi.org/10.1016/j.eswa.2005.07.022 (2006).

Article   Google Scholar  

Manikandan, K. Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization. Pattern Recogn. Lett. 125 , 432–438. https://doi.org/10.1016/j.patrec.2023.03.011 (2019).

Article   ADS   Google Scholar  

Bajwa, J., Munir, U., Nori, A. & Williams, B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc. J. 8 , 188–194. https://doi.org/10.7861/fhj.2021-0095 (2021).

Ahsan, M. M., Luna, S. A. & Siddique, Z. Machine-learning-based disease diagnosis: A comprehensive review. Healthcare 10 , 541. https://doi.org/10.3390/healthcare10030541 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Ali, O. et al. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. J. Innov. Knowl. 8 , 100333. https://doi.org/10.1016/j.jik.2023.100333 (2023).

Mirbabaie, M., Stieglitz, S. & Frick, N. R. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol. 11 , 693–773. https://doi.org/10.1007/s12553-021-00555-5 (2021).

Woodman, R. J. & Mangoni, A. A. A comprehensive review of machine learning algorithms and their application in geriatric medicine: Present and future. Aging Clin. Exp. Res. 35 , 2363–2397. https://doi.org/10.1007/s40520-023-02552-2 (2023).

Poudel, S. A study of disease diagnosis using machine learning. Med. Sci. Forum 10 , 8–20. https://doi.org/10.3390/IECH2022-12311 (2022).

Kumar, Y., Koul, A., Singla, R. & Ijaz, M. F. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J. Ambient Intell. Humaniz. Comput. 1 , 1–28. https://doi.org/10.1007/s12652-021-03612-z (2022).

Ferdous M., Debnath J. and Chakraborty N.R., (2020). Machine learning algorithms in healthcare: A literature survey. In 2020 11th International conference on computing, communication and networking technologies 1–6. https://doi.org/10.1109/ICCCNT49239.2020.9225642

Fatima, M. & Pasha, M. Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 9 , 1–16. https://doi.org/10.4236/jilsa.2017.91001 (2017).

Burkart, N. & Huber, M. F. A survey on the explain ability of supervised machine learning. J. Artif. Intell. Res. 70 , 245–317. https://doi.org/10.1613/jair.1.12228 (2021).

Dowdell, J. et al. Intervertebral disk degeneration and repair. Neurosurgery 80 , S46. https://doi.org/10.1093/neuros/nyw078 (2017).

Flores, A. M. et al. Unsupervised learning for automated detection of coronary artery disease subgroups. J. Am. Heart Assoc. 10 , e021976. https://doi.org/10.1161/JAHA.121.021976 (2021).

Chauhan T., Rawat S., Malik S. and Singh P., (2021). March. Supervised and unsupervised machine learning based review on diabetes care. In 2021 7th International Conference on Advanced Computing and Communication Systems, 1, 581–585. IEEE. https://doi.org/10.1109/ICACCS51430.2021.9442021

Lim, S., Tucker, C. S. & Kumara, S. An unsupervised machine learning model for discovering latent infectious diseases using social media data. J. Biomed. Inform. 66 , 82–94. https://doi.org/10.1016/j.jbi.2016.12.007 (2017).

Article   PubMed   Google Scholar  

Shomorony, I. et al. An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med. 12 , 1–14. https://doi.org/10.1186/s13073-019-0705-z (2020).

Bose, E. & Radhakrishnan, K. Using unsupervised machine learning to identify subgroups among home health patients with heart failure using telehealth. CIN Comput. Inform. Nurs. 36 , 242–248. https://doi.org/10.1097/CIN.0000000000000423 (2018).

Callahan, A. & Shah, N. H. Machine learning in healthcare. In Key Advances in Clinical Informatics (eds Callahan, A. & Shah, N. H.) 279–291 (Elsevier, 2017).

Chapter   Google Scholar  

Talukdar, J., Gogoi, D. K. & Singh, T. P. A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and adolescents. Healthc. Anal. 3 , 100178. https://doi.org/10.1016/j.health.2023.100178 (2023).

Brossette, S. E. et al. Association rules and data mining in hospital infection control and public health surveillance. J. Am. Med. Inform. Assoc. 5 , 373–381. https://doi.org/10.1136/jamia.1998.0050373 (1998).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sarıyer, G. & Öcal, T. C. Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining. Health Inform. J. 26 , 1177–1193. https://doi.org/10.1177/1460458219871135 (2020).

Happawana, K. A. & Diamond, B. J. Association rule learning in neuropsychological data analysis for Alzheimer’s disease. J. Neuropsychol. 16 , 116–130. https://doi.org/10.1111/jnp.12252 (2022).

Miswan, N. H., Sulaiman, I. M., Chan, C. S. & Ng, C. G. Association rules mining for hospital readmission: A case study. Mathematics 9 , 2706. https://doi.org/10.3390/math9212706 (2021).

Tandan, M., Acharya, Y., Pokharel, S. & Timilsina, M. Discovering symptom patterns of COVID-19 patients using association rule mining. Comput. Biol. Med. 131 , 104249. https://doi.org/10.1016/j.compbiomed.2021.104249 (2021).

Dehghani, M. & Yazdanparast, Z. Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining. Inform. Med. Unlocked 42 , 101351. https://doi.org/10.1016/j.imu.2023.101351 (2023).

Khafaga, D. S., Alharbi, A. H., Mohamed, I. & Hosny, K. M. An integrated classification and association rule technique for early-stage diabetes risk prediction. Healthcare 10 , 2070. https://doi.org/10.3390/healthcare10102070 (2022).

Cui, J., Zhao, S. and Sun, X., (2022). An association rule mining algorithm for clinical decision support. In Proceedings of the 8th International Conference on Computing and Artificial Intelligence , 1, 137–143. https://doi.org/10.1145/3532213.3532234 .

Péran, P. et al. MRI supervised and unsupervised classification of Parkinson’s disease and multiple system atrophy. Mov. Disord. 33 (4), 600–608. https://doi.org/10.1002/mds.27307 (2018).

Ma, E. Y. et al. Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea. Sci. Rep. 11 (1), 4457. https://doi.org/10.1038/s41598-021-84003-4 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Cai, M., Li, J., Nali, M., & Mackey, T. K. (2021, June). Evaluation of hybrid unsupervised and supervised machine learning approach to detect self-reporting of COVID-19 symptoms on Twitter. In 2021 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1–6). https://doi.org/10.1109/ICCWorkshops50388.2021.9473830 .

Sáiz-Manzanares, M. C. et al. Use of digitalisation and machine learning techniques in therapeutic intervention at early ages: Supervised and unsupervised analysis. Children 11 (4), 381. https://doi.org/10.3390/children11040381 (2024).

Ahmed, K. et al. Early detection of lung cancer risk using data mining. Asian Pac. J. Cancer Prev. 1 , 595–598. https://doi.org/10.7314/APJCP.2013.14.1.595 (2013).

Hasan, S. M. M., Mamun, M. A., Uddin, M. P. & Hossain, M. A. Comparative analysis of classification approaches for heart disease prediction. Int. Conf. Comput. Commun. Chem. Mater. Electron. Eng. https://doi.org/10.1109/IC4ME2.2018.8465594 (2018).

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This work was financially supported by the Research Deputy of Education and Research, University of Torbat Heydarieh. Grant number: 212.

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Sogandi, F. Identifying diseases symptoms and general rules using supervised and unsupervised machine learning. Sci Rep 14 , 17956 (2024). https://doi.org/10.1038/s41598-024-69029-8

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Received : 02 March 2024

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DOI : https://doi.org/10.1038/s41598-024-69029-8

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