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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 - 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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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

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

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

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

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

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

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

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

 Statistics

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

Research bias

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

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

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

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

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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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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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Biology archive

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

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Video transcript

1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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Scientific Reasoning

Scientific reasoning is the foundation supporting the entire structure of logic underpinning scientific research.

This article is a part of the guide:

• Falsifiability
• Inductive Reasoning
• Deductive Reasoning
• Hypothetico-Deductive Method
• Testability

Browse Full Outline

• 1 Scientific Reasoning
• 2.1 Falsifiability
• 2.2 Verification Error
• 2.3 Testability
• 2.4 Post Hoc Reasoning
• 3 Deductive Reasoning
• 4.1 Raven Paradox
• 5 Causal Reasoning
• 6 Abductive Reasoning
• 7 Defeasible Reasoning

It is impossible to explore the entire process, in any detail, because the exact nature varies between the various scientific disciplines.

Despite these differences, there are four basic foundations that underlie the idea, pulling together the cycle of scientific reasoning.

Observation

Most research has real world observation as its initial foundation. Looking at natural phenomena is what leads a researcher to question what is going on, and begin to formulate scientific questions and hypotheses .

Any theory, and prediction, will need to be tested against observable data.

Theories and Hypotheses

This is where the scientist proposes the possible reasons behind the phenomenon, the laws of nature governing the behavior.

Scientific research uses various scientific reasoning processes to arrive at a viable research problem and hypothesis. A theory is generally broken down into individual hypotheses, or problems, and tested gradually.

Predictions

A good researcher has to predict the results of their research, stating their idea about the outcome of the experiment, often in the form of an alternative hypothesis .

Scientists usually test the predictions of a theory or hypothesis, rather than the theory itself. If the predictions are found to be incorrect, then the theory is incorrect, or in need of refinement.

Data is the applied part of science, and the results of real world observations are tested against the predictions.

If the observations match the predictions, the theory is strengthened. If not, the theory needs to be changed. A range of statistical tests is used to test predictions, although many observation based scientific disciplines cannot use statistics .

The Virtuous Cycle

This process is cyclical: as experimental results accept or refute hypotheses, these are applied to the real world observations, and future scientists can build upon these observations to generate further theories.

Differences

Whilst the scientific reasoning process is a solid foundation to the scientific method , there are variations between various disciplines.

For example, social science, with its reliance on case studies , tends to emphasis the observation phase, using this to define research problems and questions.

Physical sciences, on the other hand, tend to start at the theory stage, building on previous studies, and observation is probably the least important stage of the cycle.

Many theoretical physicists spend their entire career building theories, without leaving their office. Observation is, however, always used as the final proof.

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Martyn Shuttleworth (May 7, 2008). Scientific Reasoning. Retrieved Jun 18, 2024 from Explorable.com: https://explorable.com/scientific-reasoning

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35 Scientific Thinking and Reasoning

Kevin N. Dunbar, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

David Klahr, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA

• Published: 21 November 2012
• Cite Icon Cite
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Scientific thinking refers to both thinking about the content of science and the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. Here we cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research. Future research will focus on the collaborative aspects of scientific thinking, on effective methods for teaching science, and on the neural underpinnings of the scientific mind.

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Conceptual review on scientific reasoning and scientific thinking

• Published: 30 April 2021
• Volume 42 , pages 4313–4325, ( 2023 )

Cite this article

• Carlos Díaz 1 , 2 ,
• Birgit Dorner 3 ,
• Heinrich Hussmann 4 &
• Jan-Willem Strijbos 1 , 5  

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When conducting a systematic analysis of the concept of scientific reasoning (SR), we found confusion regarding the definition of the concept, its characteristics and its blurred boundaries with the concept of scientific thinking (ST). Furthermore, some authors use the concepts as synonyms. These findings raised three issues we aimed to answer in the present study: (1) are SR and ST the same concept, (2) if not, what are the differences between them, and (3) how can SR and ST be characterised and operationalised for systematic research? We conducted a conceptual review using an integrative approach to analyse 166 texts. First, we found that thinking and reasoning might refer to different processes. Likewise, SR and ST can be characterised as distinct concepts. Furthermore, the review identified that differences found between the concepts of SR and ST are grounded in ontological and epistemological perspectives.

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Díaz, C., Dorner, B., Hussmann, H. et al. Conceptual review on scientific reasoning and scientific thinking. Curr Psychol 42 , 4313–4325 (2023). https://doi.org/10.1007/s12144-021-01786-5

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

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.

Experimental Research Methods

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

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

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

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

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

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Learning Objectives

• Compare and contrast theories and hypotheses

The Process of Science

Science (from the Latin scientia, meaning “knowledge”) can be defined as knowledge that covers general truths or the operation of general laws, especially when acquired and tested by the scientific method. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses (testable statements) by means of repeatable experiments. Although using the scientific method is inherent to science, it is inadequate in determining what science is. This is because it is relatively easy to apply the scientific method to disciplines such as physics and chemistry, but when it comes to disciplines like archaeology, paleoanthropology, psychology, and geology, the scientific method becomes less applicable as it becomes more difficult to repeat experiments.

These areas of study are still sciences, however. Consider archaeology: even though one cannot perform repeatable experiments, hypotheses may still be supported. For instance, an archaeologist can hypothesize that an ancient culture existed based on finding a piece of pottery. Further hypotheses could be made about various characteristics of this culture. These hypotheses may be found to be plausible (supported by data) and tentatively accepted, or may be falsified and rejected altogether (due to contradictions from data and other findings). A group of related hypotheses, that have not been disproven, may eventually lead to the development of a verified theory. A theory is a tested and confirmed explanation for observations or phenomena that is supported by a large body of evidence. Science may be better defined as fields of study that attempt to comprehend the nature of the universe.

Scientific Reasoning

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. To do this, they use two methods of logical thinking: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative or quantitative and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies provide an example. In this type of research, many live brains are observed while people are doing a specific activity, such as viewing images of food. The part of the brain that “lights up” during this activity is then predicted to be the part controlling the response to the selected stimulus; in this case, images of food. The “lighting up” of the various areas of the brain is caused by excess absorption of radioactive sugar derivatives by active areas of the brain. The resultant increase in radioactivity is observed by a scanner. Then researchers can stimulate that part of the brain to see if similar responses result.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reason, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to forecast specific results. From those general principles, a scientist can extrapolate and predict the specific results that would be valid as long as the general principles are valid. Studies in climate change can illustrate this type of reasoning. For example, scientists may predict that if the climate becomes warmer in a particular region, then the distribution of plants and animals should change. These predictions have been written and tested, and many such predicted changes have been observed, such as the modification of arable areas for agriculture correlated with changes in the average temperatures.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science, which is usually inductive, aims to observe, explore, and discover, while hypothesis-based science, which is usually deductive, begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred and most scientific endeavors combine both approaches. The fuzzy boundary becomes apparent when thinking about how easily observation can lead to specific questions. For example, a gentleman in the 1940s observed that the burr seeds that stuck to his clothes and his dog’s fur had a tiny hook structure. Upon closer inspection, he discovered that the burrs’ gripping device was more reliable than a zipper. He eventually developed a company and produced the hook-and-loop fastener popularly known today as Velcro. Descriptive science and hypothesis-based science are in continuous dialogue.

• A hypothesis is a statement/prediction that can be tested by experimentation.
• A theory is an explanation for a set of observations or phenomena that is supported by extensive research and that can be used as the basis for further research.
• Inductive reasoning draws on observations to infer logical conclusions based on the evidence.
• Deductive reasoning is hypothesis-based logical reasoning that deduces conclusions from test results.
• theory : a well-substantiated explanation of some aspect of the natural world based on knowledge that has been repeatedly confirmed through observation and experimentation
• hypothesis : a tentative conjecture explaining an observation, phenomenon, or scientific problem that can be tested by further observation, investigation, and/or experimentation

Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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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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What's the difference between deductive reasoning and inductive reasoning?

Deductive reasoning and inductive reasoning are easy to mix up. Learn what the difference is and see examples of each type of scientific reasoning.

Sherlock Holmes, the fictional sleuth who famously resides on Baker Street, is known for his impressive powers of logical reasoning. With a quick visual sweep of a crime scene, he generates hypotheses, gathers observations and draws inferences that ultimately reveal the responsible criminal's methods and identity.

Holmes is often said to be a master of deductive reasoning, but he also leans heavily on inductive reasoning. Because of their similar names, however, these concepts are easy to mix up.

So what's the difference between deductive and inductive reasoning? Read on to learn the key distinctions between these two modes of logic used by literary detectives and real-life scientists alike. 

Related: Sherlock Holmes' famous memory trick really works  

What is deductive reasoning?

Deductive reasoning, also known as deduction, is a basic form of reasoning that uses a general principle or premise as grounds to draw specific conclusions. 

This type of reasoning leads to valid conclusions when the premise is known to be true — for example, "all spiders have eight legs" is known to be a true statement. Based on that premise, one can reasonably conclude that, because tarantulas are spiders, they, too, must have eight legs.

The scientific method uses deduction to test scientific hypotheses and theories , which predict certain outcomes if they are correct, said Sylvia Wassertheil-Smoller , a researcher and professor emerita at Albert Einstein College of Medicine. 

"We go from the general — the theory — to the specific — the observations," Wassertheil-Smoller told Live Science. In other words, theories and hypotheses can be built on past knowledge and accepted rules, and then tests are conducted to see whether those known principles apply to a specific case.

Deductive reasoning begins with a first premise, which is followed by a second premise and an inference, or a conclusion based on reasoning and evidence. A common form of deductive reasoning is the "syllogism," in which two statements — a major premise and a minor premise — together reach a logical conclusion. 

For example, the major premise "Every A is B" could be followed by the minor premise "This C is A." Those statements would lead to the conclusion that "This C is B." Syllogisms are considered a good way to test deductive reasoning to make sure the argument is valid.

In deductive reasoning, if something is true of a class of things in general, it is also true for all members of that class. 

Deductive conclusions are reliable provided that the premises they're based on are true, but you run into trouble if they're false, according to Norman Herr , a professor of secondary education at California State University, Northridge. For instance the argument "All bald men are grandfathers. Harold is bald. Therefore, Harold is a grandfather," is logically valid, but it is untrue because the original premise is false.

Related: Crows outthink monkeys, can grasp recursive patterns  

Deductive reasoning examples

Here are some examples of deductive reasoning:

Major premise: All mammals have backbones. Minor premise: Humans are mammals. Conclusion: Humans have backbones.

Major premise: All birds lay eggs. Minor premise: Pigeons are birds. Conclusion: Pigeons lay eggs.

Major premise: All plants perform photosynthesis. Minor premise: A cactus is a plant. Conclusion: A cactus performs photosynthesis. 

What is inductive reasoning?

Inductive reasoning uses specific and limited observations to draw general conclusions that can be applied more widely. So while deductive reasoning is more of a top-down approach — moving from a general premise to a specific case — inductive reasoning is the opposite. It uses a bottom-up approach to generate new premises, or hypotheses, based on observed patterns, according to the University of Illinois .

Inductive reasoning is also called inductive logic or inference. "In inductive inference, we go from the specific to the general," Wassertheil-Smoller told Live Science. "We make many observations, discern a pattern, make a generalization, and infer an explanation or a theory." 

In science, she added, there is a constant interplay between inductive and deductive reasoning that leads researchers steadily closer to a truth that can be verified with certainty, 

The reliability of a conclusion made with inductive logic depends on the completeness of the observations. For instance, let's say you have a bag of coins; you pull three coins from the bag, and each coin is a penny. Using inductive logic, you might then propose that all of the coins in the bag are pennies.

Even though all of the initial observations — that each coin taken from the bag was a penny — are correct, inductive reasoning does not guarantee that the conclusion will be true. The next coin you pull could be a quarter. 

Here's another example: " Penguins are birds. Penguins can't fly. Therefore, no birds can fly." The conclusion does not follow logically from the statements, because the only birds included in the sample were penguins.

Despite this inherent limitation, inductive reasoning has its place in the scientific method, and scientists use it to form hypotheses and theories. Researchers then use deductive reasoning to apply the theories to specific situations.

Related: Does everyone have an inner monologue?  

Inductive reasoning examples

Here are some examples of inductive reasoning:

Data: I see fireflies in my backyard every summer. Hypothesis: This summer, I will probably see fireflies in my backyard.

Data: I tend to catch colds when people around me are sick. Hypothesis: Colds are infectious.

Data: Every dog I meet is friendly. Hypothesis: Most dogs are usually friendly. 

What is abductive reasoning?

Another form of scientific reasoning that diverges from inductive and deductive reasoning is called abductive. Abductive reasoning is a form of logic that starts with an incomplete set of observations and proceeds to the likeliest possible explanation for that data, according to Butte College in Oroville, California. 

It is based on making and testing hypotheses using the best information available. It often entails making an educated guess after observing a phenomenon for which there is no clear explanation. 

For example, a person walks into their living room and finds torn-up papers all over the floor. The person's dog has been alone in the apartment all day. The person concludes that the dog tore up the papers because it is the most likely scenario. It's possible that a family member with a key to the apartment swung by and destroyed the papers, or it may have been done by the landlord. But the dog theory is the most likely conclusion based on the data at hand. 

Abductive reasoning is useful for forming hypotheses to be tested. For instance, abductive reasoning is used by doctors when they're assessing which ailment a patient likely has based on their symptoms. They then check which potential diagnosis is correct using medical tests. Jurors also use abductive reasoning to make decisions based on the select evidence presented to them by lawyers and witnesses.

Related: What is Occam's razor?  

Abductive reasoning examples

Here are some examples of abductive reasoning:

— How many calories can the brain burn by thinking?

— What is the Dunning-Kruger effect?

— Can we ever stop thinking?

Observation: The grass is wet outside when you get up in the morning, but you haven't recently watered the lawn. Best-guess explanation: It likely rained last night.

Observation: At a restaurant, you see a bag and a half-eaten sandwich at an empty table. Best-guess explanation: The table's occupant is probably in the restroom.

Observation: You enter a basketball court and see a group of people in red shirts celebrating while another group in blue shirts sulks. Best-guess explanation: The red team probably just beat the blue team in a game.

Editor's note: This article was updated on March 7, 2024.

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Scientific Revolution

The Scientific Revolution (1500-1700), which occurred first in Europe before spreading worldwide, witnessed a new approach to knowledge gathering – the scientific method – which utilised new technologies like the telescope to observe, measure, and test things never seen before. Thanks to the development of dedicated institutions, scientists conducted yet more experiments and shared their knowledge, making it ever more accurate. By the end of this 'revolution', science had replaced philosophy as the dominant method of acquiring new knowledge and improving the human condition.

Defining a 'Revolution'

Dating the beginning and end of the Scientific Revolution is problematic. Historians do not all agree on precise dates as the 'revolution' was not a single dramatic event but, rather, a long and gradual series of discoveries and changes in attitudes to knowledge. The period of the 16th and 17th centuries as a whole generally covers most of the pertinent events and discoveries. There is also the problem of what to call these events. This was not a 'revolution' in the usual sense of the term, that is, a movement involving all classes, in all places, over a short space of time with a defined end goal which was ultimately achieved. Rather, from around 1500 to around 1700, there was a gradual but marked shift in how thinkers approached the acquisition of knowledge of the world around us. Modern historians often shy away from using such a dramatic term as 'revolution' to describe any deep change in human behaviour, since such a blanket term caries with it uncalled-for baggage of meanings and masks a number of anomalies, not least in this case that the 'revolution' was never complete or completed. That something momentous did occur is, however, clear from even the briefest assessment of how knowledge was gathered before and how it has been gathered ever since the Scientific Revolution.

Through the two centuries of the Scientific Revolution, natural philosophers who still adhered to ancient wisdom were slowly replaced in importance by practical scientists who used scientific instruments like the telescope and barometer to test their hypotheses and then share and review their findings. In this way, universal laws could be formed which were then further tested and used to predict outcomes in yet more experiments. Mathematics, in particular, came to dominate thought as more traditional methods of pursuing knowledge like magic, alchemy , and astrology were sidelined in favour of more objective, empirical, and evidence-based experimentation. In addition, the great trio of ancient thinkers who had held sway right through the Middle Ages – Aristotle (l. 384-322 BCE), Claudius Ptolemy (c. 100 to c. 170 CE), and Galen (129-216 CE) – were swept away as early modern minds finally looked to the future instead of the past.

Instruments like the pendulum clock and thermometer made it possible to accurately measure the world around us while optical instruments revealed things previously unimaginable such as the real nature of the surface of the Moon and the intricate anatomy of tiny insects. In all of these senses, then, there was indeed a 'revolution' that resulted in old theories, many of which had been held since antiquity as true, being cast aside and brand new ones replacing them based on new discoveries, new methodologies, and entirely new fields of study.

The Scientific Method

A distinctive feature of the change in thought during the Scientific Revolution was a reconsideration of how new knowledge should be acquired and tested. Practical experiments had been conducted ever since antiquity, but through the Middle Ages, a certain theoretical approach to knowledge, first pioneered by thinkers like Aristotle, had come to dominate. Verbal arguments had become more important than what could actually be seen in the world. Further, natural philosophers had become preoccupied with why things happen instead of first ascertaining what was actually happening in nature and how it was happening. One of the first to question this approach was the English statesman and philosopher Francis Bacon (1561-1626).

Bacon called for a more systematic and practical approach where empirical (observable) consequences of experiments were collated, assessed using reason, and then openly shared for review by other thinkers. The ultimate objective of this activity should be used to test the validity of existing knowledge and forge a new understanding of the world around us so that the human condition can be practically improved. For these reasons, Bacon is considered one of the founders of modern scientific research and scientific method, even as "the father of modern science". Bacon's approach did become a reality, but with important additions such as the use of a hypothesis as part of the experimental process, the application of mathematics to create universal laws, and the addition of new technology that greatly improved the senses.

The scientific method came to involve the following key components:

• conducting practical experiments
• conducting experiments without prejudice of what they should prove
• using deductive reasoning (creating a generalisation from specific examples) to form a hypothesis (untested theory), which is then tested by an experiment, after which the hypothesis might be accepted, altered, or rejected based on empirical (observable) evidence
• conducting multiple experiments and doing so in different places and by different people to confirm the reliability of the results
• an open and critical review of the results of an experiment by peers
• the formulation of universal laws (inductive reasoning or logic) using, for example, mathematics
• a desire to gain practical benefits from scientific experiments and a belief in the idea of scientific progress

(Note: the above criteria are expressed in modern linguistic terms, not necessarily those terms 17th-century scientists would have used since the revolution in science also caused a revolution in the language to describe it.)

Important Inventions

The Scientific Revolution witnessed a great number of new inventions, that is, technological innovations that allowed the new scientists to not only discover new things about the world but also ways to measure, test, and assess these new phenomena. The most important inventions in the Scientific Revolution include:

• the telescope (c. 1608)
• the microscope (c. 1610)
• the barometer (1643)
• the thermometer (c. 1650)
• the pendulum clock (1657)
• the air pump (1659)
• the balance spring watch (1675)

Important Discoveries

With the above inventions and others, scientists in many different countries made many new discoveries, and whole new specialisations of study became possible, such as meteorology, microscopic anatomy, embryology, and optics.

The Italian Galileo Galilei (1564-1642) built the most powerful of the early telescopes, and with it, he discovered the mountains and valleys of the Moon's surface, previously thought to be made of some unknown substance. Galileo identified four moons of the planet Jupiter and the phases of Venus . He observed sunspots, leading him to suggest the Sun was a turning sphere. The German Johannes Kepler (1571-1630) created a new type of telescope, which used two convex lenses, and he used it to observe the heavenly bodies and confirm the heliocentric view of our galaxy proposed by Nicolaus Copernicus (1473-1543 CE). At last, the geocentric model of Ptolemy was shown to be wrong. In addition, Kepler demonstrated that the planets moved in elliptical and not circular orbits.

The Italian astronomer Gian Domenico Cassini (1625-1712) identified the spaces in the rings of Saturn . Johannes Hevelius (1611-1687) in Danzig (modern Gdańsk) discovered the first variable star and created a detailed map of the Moon's surface. The English astronomer Edmond Halley (1656-1742) established an observatory on the island of St. Helena in the South Atlantic in 1677 and created the first chart of the southern stars using a telescope. Halley also discovered the acceleration of the Moon, noted the movement of the stars in relation to each other (proper motion), and identified the comet of 1682 as the same one of 1607 and 1531.

The English scientist Isaac Newton (1642-1727) invented the reflecting telescope in 1668, which used a curved mirror. Newton discovered that white light was made up of a spectrum of coloured light, and he formed his universal theory of gravity, which explained why objects fell on earth and why the heavenly bodies move as they do.

The invention of the microscope, in many ways the natural opposite of the telescope, is usually credited to the spectacle-maker Hans Lippershey (c. 1570 to c. 1619), then living in the Netherlands. The Italian Marcello Malpighi used a microscope to discover capillaries in the blood system in 1661. This was the missing link between arteries and veins, and it confirmed William Harvey's discovery of blood circulation . Galen's views of how the human body worked were now proven to be wholly inadequate or plain wrong.

The English experimentalist Robert Hooke (1635-1703) used his microscope to create sensational drawings of a new miniature world published in his Micrographia in 1665. The Dutchman Antonie van Leeuwenhoek (1632-1723) pioneered a new type of microscope using a glass bead as a lens, which gave him a much greater magnification than previously possible. Leeuwenhoek discovered bacteria, protozoa, red blood cells, spermatozoa, and how minute insects and parasites reproduce. Another Dutch microscopist, Jan Swammerdam (1637-1680), discovered that caterpillars contain what become the wings of the butterfly after metamorphosis. Finally, Nehemiah Grew (1641-1712) was the founder of plant anatomy based on his in-depth study of the sexual organs of plants.

The barometer was invented in 1643 by the Italian Evangelista Torricelli (1608-1647), and it allowed scientists to understand atmospheric pressure. The Frenchman Blaise Pascal (1623-1662) used a barometer to demonstrate that air pressure changes with altitude. The German Otto von Guericke (1602-1686) noted that air pressure varied depending on the weather. The barometer was actually named by the English scientist Robert Boyle (1627-1691), who also worked on air pumps. Boyle and his associate Robert Hooke were able to demonstrate how a vacuum could exist, and they subjected all manner of specimens to changes in air pressure inside their air pump. Boyle was thus able to formulate a universal principle that became known as 'Boyle's Law '. This law states that the pressure exerted by a certain quantity of air varies inversely in proportion to its volume (provided temperatures are constant).

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A related device, the liquid thermometer, was invented in Florence around 1650, and it transformed medicine , allowing doctors to measure a patient's temperature beyond a mere 'hot', 'cold' or 'normal'. The device meant many other experiments could now be made and the results accurately measured and compared.

The first working model of the pendulum clock was invented by the Dutchman Christiaan Huygens (1629-1695) in 1657. In a pendulum clock, the regularity of the pendulum's swing precisely controls the falling of a weight. The best pendulum clocks lost a maximum of 15 seconds per day compared to 15 minutes with a mechanical clock. Timekeeping became even more accurate with the invention in 1675 of watches using a balance spring. This great leap forward in accuracy not only helped scientists better monitor their experiments and time their observations of objects in space but it also revolutionised the very idea of time for everyone. This was the first step towards having a universal time, and with it came the concepts of being early, on time, and late in daily life.

Institutionalised Science

Another key development of the Scientific Revolution, besides a new method and new technology, was the foundation of dedicated research bodies. At this time, universities (with the possible exception of departments of medicine) were not concerned with research, but only with teaching. A new type of institution was required where scientists could work together, share their findings, and, most importantly of all, receive funding for their work. These were the new academies and societies that sprang up across Europe. The first such society was the Academia del Cimento in Florence, founded in 1657. Others soon followed, notably the Royal Society in London in 1663 and the Royal Academy of Sciences in Paris in 1667. Those responsible for the foundation of the Royal Society credited Bacon with the idea, and they were keen to follow his principles of scientific method and his emphasis on sharing and communicating scientific data and results. The Berlin Academy was founded in 1700 and the St. Petersburg Academy in 1724. These academies and societies became the focal points of an international network of scientists who corresponded, read each other's works, and even visited each other's laboratories and observatories as the new scientific method took hold. The public was involved, too, either indirectly through access to published journals and books or directly with the opportunity to attend experiments and demonstrations in the societies' headquarters or out in the field.

That there was an increase in international cooperation in the Scientific Revolution is indicated in the invitation to non-nationals to become fellows of these societies. There were attempts to standardise certain experiments across borders and the instruments different scientists were using. For example, the German Daniel Gabriel Fahrenheit (1686-1736) devised his Fahrenheit scale for thermometers around 1714. Anders Celsius (1701-1744) from Sweden came up with a rival scale, but having two scales on thermometers was a vast improvement from the early days when scientists in different countries simply used their own scales, a situation that made comparisons of results extremely difficult. There was, too, cooperation between scientists despite them belonging to rival European empires, and it was through these colonial empires, especially the Dutch, French, and British, that the ideas of the Scientific Revolution spread far beyond Europe.

Reaction to the Scientific Method

The reaction to the Scientific Revolution was not all positive. Some intellectuals were sceptical that the new scientific instruments could be trusted. There remained sceptics of experimentation in general, those who stressed that the senses could be misled when the reason of the mind could not be. One such doubter was René Descartes (1596-1650), but if anything, he and other natural philosophers who questioned the value of the work of the practical experimenters were responsible for creating a lasting new division between philosophy and what we would today call science. The term "science" was still not widely used in the 17th century, instead, many experimenters referred to themselves as practitioners of "experimental philosophy". The first use in English of the term "experimental method" was in 1675. The development of these terms illustrates that a break was happening between theoretical and practical thinkers.

Some even questioned whether humanity should be delving into a previously unseen world, which they considered should remain God 's affair. There was a clash between science and religion when it came to the view of how the universe was organised. Church figures preferred to hold on to the idea that the Earth and humanity must be at the centre of the universe, and so thinkers like Galileo, who supported Copernicus ' heliocentric model, were found guilty of heresy. However, most scientists were Christians and had no wish to challenge the teaching of the Bible . Many scientists simply wanted to explain how the world was made as it is. Indeed, some argued that the telescope and microscope demonstrated just how intricate life is, and so one should, they thought, hold even more wonder at God's work.

There was still room for God in this new scientific world, since thinkers like Isaac Newton, for example, could only explain that gravity moved planets, he could not explain where gravity came from or why it existed. There were still many limits to human knowledge. Doctors now knew why certain diseases might come about but still had only limited knowledge of how to cure them. The great longitude problem of how navigators could track their position around the globe remained unsolved. Technology was still frustratingly limited in many areas.

Into the Future

New scientific instruments meant that discoveries came thick and fast, often causing bewilderment at just how complex life could be. Telescopes at one end of the scale and microscopes at the other revealed that a whole new system of measurement was required for the human mind to grasp the scale of the wonders of the visible universe. Previously, the human body had been used as a base of the measurement system, soon nanometers and light years would be required. There were momentous changes in how people of all classes viewed the new worlds opened up by the scientists. This is best seen in the popular fiction of the period, which began to discuss intriguing yet also troubling ideas like the infinity of the universe or that tiny parasites themselves had even smaller parasites, which themselves had yet smaller parasites. Could it be possible to one day travel to the Moon? Since the Earth was no longer the centre of the universe, did this not mean there could be other planets with other life forms?

There was, though, amongst this perplexity, a new confidence and belief, certainly amongst the scientists, that technology and science, given time, could provide all the answers humanity needed to live better, longer, and more happily. New clock mechanisms with their sophisticated gears, the use of pistons in air pumps, and the discovery of the power of air pressure all inspired engineers to invent new machines like the steam engine as another, even greater revolution, appeared on the horizon: the British Industrial Revolution .

The Scientific Revolution had another lasting effect, and that is the establishment of science as the most recognised method of finding truth, a position of dominance it still holds today. When we talk about theories, hypotheses, laws of nature, evidence, facts, and progress we use terms which were coined during the Scientific Revolution; to discuss knowledge today without using these terms is unthinkable, and there, perhaps, lies the true legacy of this revolution in ideas, methods, and technology.

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Bibliography

• Burns, William E. The Scientific Revolution in Global Perspective. Oxford University Press, 2015.
• Burns, William E. The Scientific Revolution. ABC-CLIO, 2001.
• Bynum, William F. & Browne, Janet & Porter, Roy. Dictionary of the History of Science . Princeton University Press, 1982.
• Fermi, Laura & Bernardini, Gilberto. Galileo and the Scientific Revolution. Dover Publications, 2013.
• Gleick, James. Isaac Newton. Vintage, 2004.
• Henry, John. The Scientific Revolution and the Origins of Modern Science . Red Globe Press, 2008.
• Jardine, Lisa. Ingenious Pursuits. Nan A. Talese, 1999.
• Wootton, David. The Invention of Science. Harper, 2015.

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Empowering every scientist with AI-augmented scientific discovery

Jun 18, 2024 | Jason Zander - EVP, Strategic Missions and Technologies

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At Microsoft, our vision is to empower scientists with the latest breakthroughs in AI to unlock their full creative potential and tackle some of our most pressing challenges. This vision will require bringing the full power of generative AI together with quantum-classical hybrid computing to augment every stage of the scientific method. Whether expanding knowledge research, creating better hypotheses, or accelerating experimentation and analyses, doing so demands a purpose-built cloud platform for science. This is why we built Azure Quantum Elements for chemistry and materials science.

Today, we’re announcing Generative Chemistry and Accelerated DFT, which will expand the ways researchers can harness the full power of this platform. These breakthrough capabilities will empower scientists to compress the next 250 years of chemistry into the next 25.

With Generative Chemistry, we want to broaden the horizons of scientific exploration. Researchers can generate and explore novel molecules suited for specific industry applications using the latest AI models trained on hundreds of millions of compounds, and then evaluate the steps suggested by the workflow for synthesizing the most promising candidates in a lab more efficiently — all in a matter of days rather than years.

With Accelerated DFT, researchers can expedite and scale their chemical discovery pipelines by simulating the quantum-mechanical properties of molecules at an unprecedented speed — an order of magnitude faster compared to other Density Functional Theory (DFT) codes.

This brings us closer to a new paradigm for scientific discovery, where advanced AI and digital tools are more accessible than ever to scientists, students, and labs across industries. Below is our vision for how researchers will be able to leverage these breakthrough capabilities to design new molecules and enable the transformation of entire sectors from consumer goods and medicine, to manufacturing and energy, in turn addressing some of our most pressing societal challenges.

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We’re working towards this vision today. As part of the private preview of Azure Quantum Elements, scientists and developers have the opportunity to explore Accelerated DFT today, with the potential to access Generative Chemistry in the coming weeks.

We’re already putting our vision into practice by collaborating with Unilever, a global leader in consumer goods, which serves over 3.4 billion people every single day. Unilever is harnessing the power of Microsoft supercomputing and AI services to support their digital R&D transformation and product innovation.

Integrating AI into every stage of the scientific method

From global ambitions like reversing climate change and pioneering renewable energy sources to personal ones like living more sustainably and using healthier and safer products, we all want to do our part to create a better world. Time is of the essence for many of these goals, with more than 8 million scientists 1  around the globe working to pioneer innovative solutions and unlock progress. At Microsoft, we aim to empower them with state-of-the-art digital tools to harness the full collective ingenuity of every researcher and lab around the world.

Just as generative AI has unleashed new waves of creativity and improved productivity with collaborative tools like Copilot, we are now bringing AI and natural language processing capabilities to science. Our goal is to integrate AI reasoning into every stage of the scientific method: this requires the power of next-generation AI models to speed up the scientific process from hypothesis to results. It starts with knowledge research and hypothesis generation, connecting the dots by generating millions of potential molecular candidate solutions, then narrowing down candidates with digital experiments and analyzing the outcomes — all in a matter of days. We demonstrated how this approach can land real-world results in our collaboration with PNNL, where we screened over 32 million candidates to discover and synthesize a new material that holds the potential for better batteries — a tangible example of the possibilities in this new era of scientific discovery.

When powered by natural language tools, this new paradigm will help create an autonomous reasoning loop with AI at every stage as a scientific assistant. It will redefine how we approach innovation by democratizing these capabilities for breakthrough discoveries.

Announcing new capabilities in Azure Quantum Elements

Generative Chemistry will unleash a new wave of creativity for scientists tasked with discovering and designing new molecules . This will enable breakthrough growth across many industries, whether helping an oil and gas company discover a stronger fuel additive for enhancing the longevity of engine life, or an adhesive firm creating a new chemical for strengthening adhesion while removing unwanted residue.

We could compare this discovery process to searching for a small box in a large, crowded and dark warehouse with one small flashlight. We can only focus the light on a small area at a time while the rest of the warehouse remains completely dark and unknown. Generative AI gives us a much smarter light that can point in new directions, providing visibility where we may not have considered – or have been able – to look before.

Researchers can ask Generative Chemistry for molecules with desired characteristics, such as the ability to degrade rapidly or be recycled more easily. They can also provide information about their targeted application and let the system help determine relevant molecular properties. After a few more steps, they receive a set of candidates — matching those parameters — for further study.

However, simply generating candidates is not sufficient for transforming the discovery process with AI. The essential criteria for computational tools in chemistry are that they help scientists discover molecules that are novel, synthesizable and useful in the real world. This is why I’m excited to see our approach to Generative Chemistry come to life, suggesting molecules that have not been seen before, with useful properties tuned for a specific application, and whose synthesis is feasible in a reasonable number of steps.

For this reason, Generative Chemistry will offer researchers potential steps to consider as they develop their “recipe” for synthesizing these molecular candidates in a laboratory. Support for this critical component has been developed from the capabilities of our AutoRXN software , exploring chemical reactions in reverse order, which can help to evaluate synthesis pathways for creating a target molecule.

This capability is truly groundbreaking for scientific discovery. Businesses and research groups can look for efficient, cost-effective and innovative methods to develop new molecules in a matter of days, compressing the iterative process of extensive database searches and trial-and-error laboratory experiments. This end-to-end workflow will provide scientists with entirely new compounds that could lead to the next breakthrough in manufacturing, medicine and more.

We’re also announcing Accelerated DFT to offer a simplified and more powerful quantum chemistry solution for scientists. For the past few decades, DFT has been an extremely popular method used across a variety of molecular simulations, helping researchers to simulate and study the electronic structure of atoms, molecules and nanoparticles, as well as surfaces and interfaces.

We can liken molecular systems to traffic systems, where cars moving in various directions at different speeds represent electrons. From a traffic helicopter, we can observe the overall flow of traffic even if we don’t know each car’s speed and destination. DFT provides this “helicopter view’” of molecular systems, simplifying the complex task of tracking individual electrons by instead mapping out the “density” of them at a higher altitude.

Such DFT simulations can be complex to optimize and run, and often require supercomputer-scale resources. This is why our managed DFT service, based on innovation developed by Microsoft Research, enables researchers to perform substantially faster calculations than other DFT codes and offers a 20-fold average increase in speed compared to PySCF, a widely used open-source DFT code.

Accelerated DFT is already used by many organizations such as AspenTech, DTU Energy University of Denmark and Unilever. It seamlessly integrates into broader chemistry and materials science workflows, and paves the way for expediting innovations in therapeutics, environmental sustainability and beyond.

You can learn more about this announcement in the technical blog, Introducing two powerful new capabilities in Azure Quantum Elements: Generative Chemistry and Accelerated DFT.

Pioneering a new scientific discovery paradigm with Unilever

Unilever stands at the forefront of the consumer goods industry, with a strong portfolio of household brands that are used by 3.4 billion people every day, including Dove, TRESemmé, Omo, Degree, Hellmann’s and Ben & Jerry’s. Whether cleaning, beauty or care products, each requires the latest scientific breakthroughs to ensure the best possible consumer experience and enhance daily life.

Over the past two and a half years, Unilever has worked with Microsoft to identify new digital capabilities to drive product innovation forward. Unilever is bringing its digital vision to life through the transformational DataLab — its digital counterpart to the company’s physical laboratories — with the help of Microsoft Azure. From unlocking the secrets of our skin’s microbiome to reducing the carbon footprint of a multi-billion-dollar business, Unilever is redefining what it means to be a consumer goods company in the modern world with leading science.

With Copilot and the advanced simulation capabilities of Azure Quantum Elements, Unilever can query scientific information using natural language, performing thousands of computational simulations in the time it would take to run tens of laboratory experiments. Unilever scientists can use the data gathered from these simulations to fine-tune models that screen tens of thousands of materials at substantial speed or enable the exploration of intricate chemical reactions.

For example, R&D teams can expand their search space for novel molecules that restore natural bonds in hair fibers across more hair types, in turn redefining the standards of personalized hair care for brands like Dove and TRESemmé. Furthermore, by placing scaled simulations at the forefront of the discovery funnel, Unilever will be further empowered to expedite the delivery of solutions within their key sustainability focus areas.

“Digital tools are unlocking an unprecedented age of scientific discovery. Using advanced computing power and AI, we are able to compress decades of lab work into days, accessing a level of insight we could not previously have imagined. This technological leap , coupled with our vast repository of proprietary data and a century of expertise in personal and household care, means our scientists are able to lead the industry in developing the next generation of consumer goods.” — Alberto Prado, Global Head of R&D Digital and Partnerships at Unilever

Expanding quantum capabilities in Azure Quantum Elements

We stand on the cusp of unprecedented innovation, and at Microsoft, we continue to pioneer state-of-the-art solutions to usher in a new era of scientific discovery. We remain focused on achieving scaled quantum computing and more breakthroughs on our path to engineering our topological qubits with inherent hardware-level stability.

Earlier this year, we demonstrated with Quantinuum the most reliable logical qubits on record , further advancing the state-of-the-art for quantum computing. And recently, we simulated a chemical catalyst combining classical supercomputers, AI and logical qubits created with Microsoft’s qubit-virtualization system and Quantinuum’s H1 hardware. This combination holds the key to unlocking scientific breakthroughs enabled by a new generation of hybrid-computing applications.

In the coming months, we will bring advanced logical qubit capabilities using our software and Quantinuum’s hardware in private preview in Azure Quantum Elements. As logical qubit capabilities scale to deliver increasingly reliable results, we will unlock simulation accuracy, moving us from scientific advantage to commercial advantage, and ultimately to solving some of the world’s most pressing problems.

Accelerating scientific discovery, together

We’re committed to advancing these technologies responsibly, always focusing on innovation, empowerment and trust. That’s why we are committed to responsible computing practices and the Microsoft AI principles , to help ensure that safety measures adequately account for the increasing power of AI and quantum.

For more information about today’s announcements:

• Sign up to learn more about Accelerated DFT and Generative Chemistry, as part of the private preview of Azure Quantum Elements.
• Read the technical blog, Introducing two powerful new capabilities in Azure Quantum Elements: Generative Chemistry and Accelerated DFT .
• Register for our upcoming ACS Webinar
• Check out the upcoming Future of Cloud webinar series about our vision to accelerate scientific discovery for business innovation.

Top image: Leaders from Unilever and Microsoft discuss the Azure Quantum Elements program.

1. “Statistics and resources | 2021 Science Report .” This translates into 8.854 million full-time equivalent (FTE) researchers by 2018”.

Tags: Accelerated DFT , AI , Azure Quantum Elements , Generative Chemistry , quantum computing

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Breaking news, scientific expert declares there is ‘zero’ evidence for natural covid-19 origin.

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A panel of scientists fiercely debated Tuesday whether COVID-19 stemmed from a laboratory accident or naturally spread from animals to humans, with one expert declaring there was “zero” evidence for a natural origin of the pandemic that killed millions around the world .

Rutgers University molecular biologist Dr. Richard Ebright said in his opening statement before the Senate Homeland Security Committee that the “large preponderance of evidence indicates SARS-CoV-2, the virus that causes COVID, entered humans through a research incident.”

Ebright, who was joined in arguing for the so-called “lab-leak theory” by Dr. Steven Quay, a former professor at Stanford University School of Medicine, added that “no — zero — secure evidence points to COVID’s natural origins.”

“The probability this actually came from nature based on these features is one in a million,” Quay concurred.

COVID-19 emerged in Wuhan, China, more than 800 miles from “the closest bats harboring SARS-CoV-2 live viruses that could have served as progenitors,” Ebright explained.

The  now-debarred Wuhan Institute of Virology  (WIV), the “global epicenter of research on bat SARS viruses,” he went on, carried out US-funded, gain-of-function research  on the viruses between 2014 and 2021.

During that research period, the WIV conducted “the world’s largest research program on bat SARS viruses” and had “the world’s largest collection of bat SARS viruses” — including “the virus most closely similar to SARS-CoV-2,” Ebright added

Additionally, the Rutgers prof said, the Wuhan lab obtained SARS viruses that had a “high pandemic potential” in the four years before COVID-19 — and just one year earlier, had run research that genetically modified the viruses “that match in detail the features of SARS-CoV-2.”

By 2015, Ebright noted, scientists at a meeting of members of the Royal Society and US National Academy of Sciences had “singled out” the WIV experiments as the “most likely of all research in the world to trigger a pandemic.”

That research was funded in part by a more than $4 million National Institutes of Health (NIH) grant to the since-suspended Manhattan-based public health nonprofit  EcoHealth Alliance , about half a million dollars of which directly flowed to WIV.

Another $815,000 was given to WIV through subgrants that originated with the US Agency for International Development (USAID) and passed through the University of California, Davis, and EcoHealth.

EcoHealth lost its status as a federal grantee for likely violating biosafety standards with its WIV project, titled “Understanding the Risk of Bat Coronavirus Emergence,” and failing to immediately report the experiments, which resulted in a modified virus that was 10,000 times more infectious in lungs, 1 million times more infectious in brains and three times more lethal in humanized lab mice.

The “smoking gun” evidence for a lab origin of COVID-19, Ebright clarified, came from a separate EcoHealth proposal to the Defense Advanced Research Projects Agency (DARPA), which was never funded, that proposed the “exact feature” of a furin cleavage site in the virus.

“SARS-CoV-2 is the only one of more than 800 known SARS viruses that possess a furin cleavage site,” he said.

EcoHealth President Dr. Peter Daszak also  downplayed the degree of Chinese cooperation  in its DARPA proposal arguing the experiments were going to take place in a North Carolina research lab “under very high safety conditions,” Quay pointed out.

But “the marginal comments in drafts — that were only obtained through [Freedom of Information Act requests] — said that Wuhan would lead the project since it was going to be “cheaper” and “faster,” Quay said.

Homeland Security Committee ranking member Sen. Rand Paul (R-Ky.) asked Ebright at one point whether his staunch support for the lab leak hypothesis was a “right-wing conspiracy” or he was a “crazy Republican partisan.”

“I’m a registered Democrat. I voted for [President] Biden. I have a Biden sign on my lawn and have a Biden bumper sticker,” Ebright began before Paul cut in to say, “All right that’s enough of that,” causing lawmakers and attendees in the hearing room to erupt with laughter.

EcoHealth has denied that the experiments constituted gain-of-function research — despite testimony from NIH principal deputy director Dr. Lawrence Tabak last month  stating that it was  — or that its work resulted in a lab accident that led to the pandemic.

An EcoHealth spokesperson disputed a part of Ebright’s testimony in a statement to The Post, saying the WIV experiments produced “genome copies, not infectious viruses,” which include “dead virus and non-infectious RNA.”

“It’s important to emphasize that the research in question has no — zero — civilian practical applications,” Ebright said of the gain-of-function experiments.

“Gain-of-function research on potential pandemic pathogens is not used and does not contribute to the development of vaccines, and is not used and does not contribute to the development of drugs,” he added.

Quay in his opening remarks said that scientists “dependent on NIH or NIAID funding may have pressure to publicly agree with orthodoxies,” such as arguing against SARS-CoV-2 escaping a research lab.

That implicated one of the panel’s other witnesses, Dr. Robert Garry , who admitted to receiving as much as $25 million in NIH funding during the hearing and authored a controversial scientific paper — prompted by then-National Institute of Allergy and Infectious Diseases (NIAID) Director Dr. Anthony Fauci — in early 2020 to debunk the lab leak theory.

Ebright in his opening remarks noted that the paper, “The proximal origin of SARS-CoV-2,” was published in March 2020 as “an opinion piece,” not backed by available evidence, and completely disproven by “private communications” of the authors that were released last year by the House Select Subcommittee on the Coronavirus Pandemic.

“Four of the authors of that paper,” he said, “in their private communications show clearly that they knew the conclusion that they state in that article is invalid.”

Ebright and other scientists have twice requested the retraction of the paper with the Rutgers professor suggesting in Tuesday’s hearing that its authors were guilty of “scientific misconduct up to and including fraud.

Fauci, then-NIH Director Dr. Francis Collins and others communicated privately on phone calls and emails with the scientific co-authors as the paper was being drafted, in what some Republican lawmakers have now referred to as a “ cover-up ” to  conceal information about the origins of COVID-19 .

“You said at the time that definitively SARS-CoV-2 is not a laboratory construct,” Sen. Josh Hawley (R-Mo.) pressed Garry, noting that agencies like the  FBI  and  Energy Department  have “concluded otherwise.”

“On the basis of this, Dr. Fauci and others cited this piece and went out to use it to mobilize our own government to censor people who ask questions,” Hawley went on. “People lost their jobs because of this. … Do you regret being part of this effort, this propaganda effort?”

“I was simply just writing a paper about our scientific opinions about where this virus came from,” Garry replied.

“Oh no, you weren’t,” Hawley shot back, before reading portions of his private email correspondence into the congressional record. “February 2nd, 2020, you wrote, ‘I really can’t think of a plausible, natural scenario where you can get from the bat virus, or one very similar to it, to this … I just can’t figure out how this gets accomplished in nature. It’s stunning. Of course, in the lab, it would be easy.’”

Both Garry, a professor and associate dean at Tulane University School of Medicine, and Gregory Koblentz, director of the Biodefense Graduate Program at George Mason University, argued against the theory during the Senate hearing.

“I firmly believe the available evidence indicates that the spillover happened naturally likely at the seafood market in Wuhan, China,” Garry testified, without immediately explaining the evidence that led him to that conclusion.

Following questions from committee chairman Gary Peters (D-Mich.), Garry argued that early cases of COVID-19 in December 2019 “painted the bullseye” around the market and samples later showed SARS-CoV-2 “commingling” with RNA and DNA samples taken from racoon dogs.

In subsequent questioning, the Tulane professor admitted that “we don’t know” whether the WIV had the virus, and “we don’t have the evidence from the Chinese” that points either way.

“I am first and foremost a scientist, and I will adhere to the scientific method, so I will continue to evaluate the evidence and reassess the validity of my scientific hypotheses regarding the origin,” Garry told panel members, adding later that he still stood by the 2020 paper arguing against the lab leak.

“Natural spillovers have multiple markets,” Quay disagreed at another point, referencing how the SARS virus ripped through China beginning in 2002 and was found in at least 11 markets.

Quay added that 192 animals were later tested and showed a 100% infection rate for SARS-CoV-1, whereas 457 animals were tested for SARS-CoV-2 and “zero were found to be infected.”

Koblentz noted that the US intelligence community remained “divided” about the origins of COVID-19, but the theory that it “was deliberately developed as a biological weapon has been unanimously rejected by all US intelligence agencies.”

Asked by Sen. Roger Marshall (R-Kan.) whether the virus “could have been used as a bioweapon, Ebright responded that SARS-CoV-1 has been identified by the US government as having “high potential for use as a bioweapon in biowarfare, bioterrorism or biocrime.”

Similarly, he said, SARS-CoV-2 is “a bioweapon agent” that “potentially could be used.”

Quay told The Post and other reporters during a roundtable discussion on Monday that he could not definitively know whether COVID-19 was a bioweapon — but pointed to two striking features of the virus.

One portion of the genome sequence, he explained, made the virus “asymptomatic” and harder to produce “antibodies” in its host.

“I can’t think of a civilian use for that,” Quay said.

The rare bipartisan congressional investigation into COVID origins was presided over by Peters and Paul, who is also a doctor.

“The COVID-19 pandemic was one of the worst public health crises that our country has ever faced,” Peters said in his opening statement. “We lost more than 1 million Americans to the virus.

“Given the likelihood that the Chinese government may never fully disclose all the information they have about the initial COVID-19 outbreak, we must use the scientific information available to better prepare for future potential pandemics,” Peters affirmed.

Paul in his opening remarks highlighted the “private doubts” of many of the lab leak opponents — who smeared those skeptical of a natural origin as “conspiracy theorists.”

“The cover up went beyond public statements, federal agencies and key officials withheld and continue to conceal crucial information from both Congress and the public,” Paul said.

[The Department of Health and Human Services] and NIH have not produced documents related to the gain – of – function research that the chairman and I requested over a year ago,” he added, “and they’re still resisting.”

Sen. Ron Johnson (R-Wis.) at one point in the hearing held up a stack of more than 50 redacted pages from more than 4,000 of Fauci’s documented NIH emails that his staff had to submit Freedom of Information Act (FOIA) requests to obtain.

“In terms of the cover-up, my guess is the smoking gun exists somewhere under these heavy redactions,” Johnson claimed.

Republican senators also faulted the Office of the Director of National Intelligence for not fully declassifying intelligence related to COVID origins, pursuant to a bill introduced by Hawley and signed into law by Biden in March 2023.

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Miami Is Entering a State of Unreality

No amount of adaptation to climate change can fix Miami’s water problems.

This article was published in partnership with Floodlight . Hank Perez, 72, was trying to get home to North Miami Beach on Wednesday afternoon last week, but the rain had other plans. Floodwaters as high as the hood of Perez’s gray Toyota Yaris stalled the car; he pulled onto the median and called for roadside assistance, but it never came. Thousands of other commuters found themselves in similar straits: About a foot and a half of water had fallen across South Florida—not the product of a hurricane or a tropical storm but of a rainstorm, dubbed Invest 90L, a deluge that meteorologists are calling a once-in-200-years event. It was the fourth such massive rainfall to smite southeastern Florida in as many years.

“Rain bombs” such as Invest 90L are products of our hotter world; warmer air has more room between its molecules for moisture. That water is coming for greater Miami and the 6 million people who live here. This glittering city was built on a drained swamp and sits atop porous limestone; as the sea keeps rising, the National Oceanic and Atmospheric Administration forecasts that South Florida could see almost 11 extra inches of ocean by 2040. Sunny-day flooding, when high tides gurgle up and soak low-lying ground, has increased 400 percent since 1998, with a significant increase after 2006; a major hurricane strike with a significant storm surge could displace up to 1 million people . And with every passing year, the region’s infrastructure seems more ill-equipped to deal with these dangers, despite billions of dollars spent on adaptation.

Thirty years ago, when the dangers of climate change were beginning to be understood but had not yet arrived in force, the creeping catastrophe facing Miami might have been averted. But as atmospheric concentrations of carbon reach levels not seen in 3 million years, politicians promise resilience while ignoring emissions; developers race to build a bounty of luxury condos , never mind the swiftly rising sea. Florida is entering a subtropical state of unreality in which these decisions don’t add up.

A massive network of canals keeps this region from reverting to a swamp, and sea-level rise is making operating them more challenging. The biggest canals, run by the South Florida Water Management District (SFWMD), offer primary drainage; smaller canals are operated by municipalities and private entities. The majority of these canals drain to the sea during low tides using gravity. But sea-level rise erodes the system’s capacity to drain water—so much so that SFWMD has already identified several main canals that need to be augmented with pumps. The scary part about last week’s flood is that it didn’t happen during particularly high tides: Less rain, or rain that fell at a gentler rate, would have drained away easily. Other adaptation initiatives are under way. Miami is overhauling how it deals with stormwater , and has installed pumps and backflow valves in vulnerable and low-lying neighborhoods. Miami Beach has spent about a decade raising roads, installing pumps, and improving its infrastructure in a multimillion-dollar effort to buy time.

But the amount of rain that did fall last week is the sort of extreme-weather event that infrastructure planners don’t design for, if only because it would be too expensive to construct stormwater systems capable of moving that much water that quickly. “Nowhere can withstand this much rain,” Bryan McNoldy, a senior researcher at the University of Miami’s Rosenstiel School of Marine, Atmospheric, and Earth Science, told me. At his home in Biscayne Park, he slept uneasily on Wednesday night after nine inches of rain fell in just 11 hours. “That’s definitely more than what my area can ingest,” he said on Friday. Just a few more inches of rain would have meant water coming up through his floorboards.

The state government isn’t exactly ignoring the rising water. Governor Ron DeSantis and his administration have attempted to address the havoc caused by the changing climate with his $1.8 billion Resilient Florida Program, an initiative to help communities adapt to sea-level rise and more intense flooding. But the governor has also signed a bill into law that would make the term climate change largely verboten in state statutes. That same bill effectively boosted the use of methane, a powerful greenhouse gas, in Florida by reducing regulations on gas pipelines and increasing protections on gas stoves. In a post on X the day he signed the bill, DeSantis called this “ restoring sanity in our approach to energy and rejecting the agenda of the radical green zealots.”

Climate researchers , for their part, refer to this strategy as “agnostic adaptation”—attempting to deal with the negative effects of climate change while advancing policies that silence discussion or ignore climate change’s causes. On Friday, at a press conference in Hollywood, Florida—which received more than 20 inches of rain—DeSantis repeated his message, emphasizing that “we don’t want our climate policy driven by climate ideology.” The Earth’s carbon cycle—which has not witnessed such a rapid increase in atmospheric carbon dioxide in the past 50,000 years—is without ideology. The carbon goes into the atmosphere, and everything that follows follows. In Miami, as the water levels rise, researchers predict that low-lying neighborhoods across the region will lose population. Eventually, Florida’s policies of agnostic adaptation will have to deal with this looming reality, where adaptation is clearly impossible, and retreat is the only option left.

5 Reasons Vision Technology Companies Are Building Brands In 2024

Vision technology continues to grow and define itself in fascinating ways.

How are you seeing growth in vision-related companies, an area that has previously appeared more stagnant?

The eyes are a window into full-body health and new technology-driven shifts further underscore the role that vision care plays in a person’s ability to be and stay healthy. The VSP ® Global Innovation Center, along with its partners at SXSW, highlighted­­ 10 vision trends that are transforming healthcare and introduced several startups emblematic of the forces shaping the industry’s future.

While vision may not be the first area to think of when it comes to healthcare startups, there are ... [+] quite a few novel developments happening.

5 Ways Vision Trends Are Reshaping Health Brands

1) The Eyes are a Window into Full-Body Health

According to research from market intelligence platform CB Insights , AI-enabled screening technologies, companies using artificial intelligence for the automated detection of various eye conditions and diseases, have raised over $1.8 billion in equity funding since 2020.

Recent developments in the field of artificial intelligence have advanced what can be detected during an eye exam. AI startups focusing on the role of eyes in disease detection include RetiSpec, which uses AI to help identify early signs of Alzheimer’s, and Toku Eyes, which has developed software to help assess heart risk using a retinal scan.

2) Smart Eyewear Accelerates Better Health

The prevalence of wearable tech ushered in the term Quantified Self, which involves self-tracking, measurement, and activation of personal health and behavioral information. Within smart eyewear, new sensors (especially those that are AI -enabled) can now take Quantified Self a bit further with more timely and meaningful personal data.

San Francisco-based Ciye has developed smart glasses that serve as a virtual coach, providing real-time feedback and insights on the user's training. The startup also makes smart goggles that use embedded sensors to track and measure a swimmer's workouts.

3) Aesthetics Bring The 'Health Spa' To Optical

Once seen as a niche category, aesthetics-focused vision care practices are gaining traction as patients are becoming more focused on the appearance of their eyes. Whether offering Intense Pulse Light (IPL) therapy to help reduce skin pigmentation or applying radiofrequency to help reduce wrinkles around the eyelids, the introduction of cosmetic services in eye care practices underscores the desire for more aesthetic-based services from patients.

In 2020, the FDA approved Upneeq, a prescription eye drop from RVL Pharmaceuticals that optometrists can apply to patients looking to improve the cosmetic appearance of droopy eyelids.

4) Accessibility Tech is a Bridge to Wider Adoption

Technologists are harnessing augmented and mixed reality to better help individuals with low vision better navigate their surroundings. Many assistive technologies, like text-to-speech and closed captioning, are seen as on-ramps to wider market adoption. Smart eyewear designed for those with vision and hearing impairment could lead to applications that enable a larger subset to improve individual productivity in the future.

As an industry leader in providing access to vision care, VSP Vision™ is constantly monitoring the pulse of healthcare transformation. The VSP ® Global Innovation Center recently published the Emerging Technology for Accessibility guide , a resource for innovators navigating the trends advancing assistive technologies. The guide is intended to spark conversation, encourage the use of inclusive design principles, and advance understanding of accessibility, low vision, and the emerging assistive device category.

Elon Musk account on Twitter and Neuralink emblem displayed on a screen in the bacground are seen in ... [+] this illustration photo taken in Krakow, Poland on February 14, 2023 (Photo by Jakub Porzycki/NurPhoto via Getty Images)

5) The Bionic Eye Makes Science Fiction A Reality

Launched by a co-founder of Neuralink, Elon Musk's brain chip startup, the Science Eye is a visual prosthesis and brain-computer platform that aims to restore sight in individuals suffering from blindness caused by age-related macular degeneration (AMD).

While the idea of integrating electronics into the human body for medical treatment has been around for decades, applications to and for the eye have remained nascent. However, research has accelerated in recent years that is making the "Bionic Eye," an electronic prosthesis that is surgically inserted into the eye, a potential reality.

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ICYMI: Microsoft President Testifies on Past Security Failures, Accountability Measures in Wake of Chinese Hack of Government Accounts

June 17, 2024

WASHINGTON, D.C. ––  Last week, the House Committee on Homeland Security, led by Chairman Mark E. Green, MD (R-TN), held  a hearing  to examine Microsoft’s security culture in the wake of the  Cyber Safety Review Board’s (CSRB) report  on the Microsoft Online Exchange 2023 cyber intrusion by Storm-0558, a threat actor affiliated with the People’s Republic of China (PRC). Witness testimony was provided by Microsoft Vice Chairman and President Brad Smith, who accepted Microsoft’s responsibility in his opening statement for the intrusion that successfully compromised 22 enterprise organizations and over 500 individuals globally, including federal government accounts, due to what the CSRB described as “a cascade of failures” by Microsoft. Chairman Green and Ranking Member Bennie Thompson (D-MS) formally requested Smith’s testimony  on May 9 .    In the hearing, members highlighted the risks associated with Microsoft’s presence in China, its approach to artificial intelligence (AI) development and deployment, Microsoft’s current and future approaches to business decisions, and the company’s plans to strengthen cybersecurity measures following the intrusion. Members also discussed the January 2024 cyber intrusion by “Midnight Blizzard,” a state-sponsored cyber actor affiliated with the Russian Foreign Intelligence Agency that was also responsible for the attack on SolarWinds in 2020.   Although the Committee commends Microsoft for announcing steps to reform its security practices, ensuring follow-through on the company’s stated commitments will be crucial for ensuring U.S. government networks and Americans––including U.S. officials––are not exposed to further risk.

In his opening statement,  Chairman Green  highlighted the broader questions the Committee must examine regarding the mitigation of economic and national security risks: “To be clear, the U.S. government would never expect a private company to work alone in protecting itself against nation-state attacks. We need to do more work to define roles and responsibilities for public and private sector actors in the event of nation-state attacks on our networks.  Our nation’s adversaries possess advanced cyber capabilities and substantial resources, often exceeding the defensive cybersecurity measures available to even the most sophisticated companies. However, we do expect government vendors to implement basic cybersecurity practices. ”   “First, closing the cyber workforce gap—my top priority for the Committee this year. The security challenges we face as a nation are compounded by the persistent shortage of cybersecurity professionals. As Microsoft continues its work to invest in our cyber workforce, we must harken back to the lessons from the CSRB report. Our cyber professionals must be trained to think of security first. We must equip them with the right skills to protect our networks and to build our systems securely. Second, we need to define the role of public and private sector entities in protecting our networks against nation-state actors. These attacks have become increasingly common, rather than anomalies. We need clearly defined responsibilities so that we can effectively respond to nation-state attacks on our networks. Finally, we must address a fundamental issue: the economic incentives that drive cybersecurity investments. As the CSRB’s report recently revealed, underinvestment in essential security measures exposed critical vulnerabilities.”

Subcommittee on Transportation and Maritime Security Chairman Carlos Gimenez (R-FL) highlighted the dangers of doing business in Communist China and asked Smith if Microsoft shares critical information on cybersecurity with the Chinese Communist Party (CCP)––as companies are required to do under Chinese law:   “This law requires all organizations and citizens to cooperate with China’s intelligence agencies, including the People’s Liberation Army, in matters of national security. While the law does not specifically mention companies working in China, it does apply to all organizations operating within the country, including foreign companies. [Do] you operate in China?”   Smith answered:    “Yes, we do”   Gimenez continued:    “Do you comply with this law?”   Smith answered:    “No, we do not”   Gimenez continued:    “How is it you got away with not complying with the law? Do you have a waiver from the Chinese government saying you don’t have to comply with this law?”   Smith answered:    “But there are many laws– there are two types of countries in the world. Those that apply every law they enact, and those that enact certain laws but don’t always apply them. And in this context, China, for that law, is in the second category.”   Gimenez continued:    “Do you really believe that because––look, I sit on the Select Committee on China, and that’s not the information that we get––that all companies in China have to cooperate with the intelligence agencies of China and the People’s Liberation Army. You operate in China, and you’re sitting there telling me that you don’t have to comply with the laws of China?”   After pressing Smith further, Gimenez later concluded:    “I’m sorry, I just––for some reason, I just don’t trust what you’re saying.”

Subcommittee on Border Security and Enforcement Chairman Clay Higgins (R-LA)  asked Smith why Microsoft did not correct, in a timely manner, its inaccurate public statements about the 2023 cyber intrusion:   “After the hack, the 2023 Microsoft Online Exchange intrusion, why did it take six months for Microsoft to update the means by which most Americans would sort of be made aware of such a hack?”   Smith answered:    “First of all, I appreciate the question, it’s one that I asked our team when I read the CSRB report. It’s the part of the report that surprised me the most. You know, we had five versions of that blog, the original, and then four updates. And we do a lot of updates of these reports. And when I asked the team, they said the specific thing that had changed, namely a theory, a hypothesis about the cause of the intrusion, changed over time. But it didn’t change in a way that would give anyone useful or actionable information that they could apply—”   Higgins continued:    “Mr. Smith, respectfully, that answer does not encourage trust. And regular Americans listening are going to have to move the tape back on the Microsoft instrument and listen to what you said again. But you didn’t do it, I mean, you’re Microsoft, [you] had a major thing happen, and the means by which you communicate with your customers was not updated for six months. So I’m just going to say that I don’t really accept your answer as thoroughly honest.”   Smith answered:    “I said the same thing, and we had the same conversation inside the company.”  

Congresswoman Laurel Lee (R-FL)  asked Smith how to improve the victim notification process in the wake of the challenges that Microsoft faced in notifying those impacted by the 2023 Storm-0558 hack:   “I’d like to hear more about one of the things that was identified in the report as an area in need of improvement––victim notification. So, I’d like for you to elaborate a little bit more on your thoughts and going forward plan on how to improve victim notification.”   Smith answered:    “When we find that someone has been a victim of an attack, it doesn’t mean that the fault was ours, it’s just that our threat detection system may have found it. We need to let them know. Well, how do you let somebody know? If it’s an enterprise, we probably have a connection, there’s probably somebody there we can call. But if it’s a consumer, like a consumer-based email system, we don’t necessarily know who the human is, we just have an email address. So, we send an email.    “There was a member of Congress we sent an email to last year. That member of Congress did what you sort of expect, they said well, that’s not really Microsoft, is it? It’s spam. […] That’s the world in which we live. And so, the CSRB has a great recommendation on this. It’s to create the equivalent of the Amber Alert. But it will require support from congress that CISA lead this, that the tech sector, and probably the telecommunications companies, and the phone makers, and the phone operating system makers all come together. This would be a huge step forward.”

Congressman Dale Strong (R-AL)  pressed Smith on any vulnerabilities still present in Microsoft’s products due to the length of time the threat actor had access to stolen credentials:   “What are the security implications of China and other potential threat actors having access into your network for so long? What is the threat of that, you know, thank goodness it was discovered, but what is the threat do you see for them being in your system for so long without being noticed?”   Smith answered:     “I would just like to qualify a little bit of the premise, because I noticed in some of the questions that were floating around this week that people suggested that because the Chinese had acquired this key in 2021 and we didn’t find it until 2023 that they must’ve had access for two years. I think that in fact they kept it in storage until they were ready to use it, knowing that once they did, it would likely be discovered quickly.”   Strong continued:    “Thank you, and that leads to my next question. Are the Chinese still able to access Microsoft’s corporate network today?”   Smith answered:    “No, not with anything they did before, and [we] do everything we can do to ensure they don’t get in any other way.”

Subcommittee on Emergency Management and Technology Chairman Anthony D’Esposito (R-NY )  asked Smith why the government should continue using its products after the CSRB questioned Microsoft’s ability to prevent future hacks without an “overhaul” of its security culture:   “Are you confident that moving forward Microsoft has the ability to quickly detect and react to an intrusion like this?”   Smith answered:    “I feel very confident that we have the strongest threat detection system that you’re going to find in, quite possibly, in any organization private or public on the planet. Will that always mean we will be the first to find everything, well no, that doesn’t work that way. But I feel very good about what we have, and I feel very confident about what we’re building.” In his closing remarks,  Chairman Green  highlighted the importance of public-private partnerships in cybersecurity and harmonizing regulations in order to prevent future intrusions:   “Sometimes government, in this public-private partnership that we talked about a couple times … sometimes the government can get in the way too, and I want to ask that you educate us as much as possible. I will give you an example. The SEC ruling, the four-day report for a breach. Some of the big cybersecurity companies, I mean the biggest in the nation, told me it [takes] seven or eight days to fix a breach. We are announcing to the world that, at four days, we have a hole in the wall, and it takes seven days to close a hole––this is the government forcing companies to invite the enemy to come in. That is a stupid regulation.    “We need help on understanding where the government also creates problems, so I would appreciate anything that comes to mind. One of the initiatives here, we talked about cyber workforce, one of the other initiatives is the synchronization of the regulations that are out there, making sure we are not duplicative, and we aren’t contradictory, because as I understand there are some regulations that are.”   “If we are causing you to have duplicitous effort, that is money that could be spent on real cybersecurity. In this partnership, we need communication, not just on the issues that are brought up here––the breach that was identified––but how we make things better and work better on how we regulate and create compliance requirements.”