![scientific method hypothesis is Encyclopedia Britannica](https://cdn.britannica.com/mendel/eb-logo/MendelNewThistleLogo.png)
- History & Society
- Science & Tech
- Biographies
- Animals & Nature
- Geography & Travel
- Arts & Culture
- Games & Quizzes
- On This Day
- One Good Fact
- New Articles
- Lifestyles & Social Issues
- Philosophy & Religion
- Politics, Law & Government
- World History
- Health & Medicine
- Browse Biographies
- Birds, Reptiles & Other Vertebrates
- Bugs, Mollusks & Other Invertebrates
- Environment
- Fossils & Geologic Time
- Entertainment & Pop Culture
- Sports & Recreation
- Visual Arts
- Demystified
- Image Galleries
- Infographics
- Top Questions
- Britannica Kids
- Saving Earth
- Space Next 50
- Student Center
![scientific method hypothesis is flow chart of scientific method](https://cdn.britannica.com/26/151526-004-FE2F64E4/Flow-chart-method.jpg)
![](http://blog10.website/777/templates/cheerup2/res/banner1.gif)
scientific method
Our editors will review what you’ve submitted and determine whether to revise the article.
- University of Nevada, Reno - College of Agriculture, Biotechnology and Natural Resources Extension - The Scientific Method
- World History Encyclopedia - Scientific Method
- LiveScience - What Is Science?
- Verywell Mind - Scientific Method Steps in Psychology Research
- WebMD - What is the Scientific Method?
- Chemistry LibreTexts - The Scientific Method
- National Center for Biotechnology Information - PubMed Central - Redefining the scientific method: as the use of sophisticated scientific methods that extend our mind
- Khan Academy - The scientific method
- Simply Psychology - What are the steps in the Scientific Method?
- Stanford Encyclopedia of Philosophy - Scientific Method
![scientific method hypothesis is flow chart of scientific method](https://cdn.britannica.com/26/151526-050-F95C5D11/Flow-chart-method.jpg?w=400&h=300&c=crop)
scientific method , mathematical and experimental technique employed in the sciences . More specifically, it is the technique used in the construction and testing of a scientific hypothesis .
The process of observing, asking questions, and seeking answers through tests and experiments is not unique to any one field of science. In fact, the scientific method is applied broadly in science, across many different fields. Many empirical sciences, especially the social sciences , use mathematical tools borrowed from probability theory and statistics , together with outgrowths of these, such as decision theory , game theory , utility theory, and operations research . Philosophers of science have addressed general methodological problems, such as the nature of scientific explanation and the justification of induction .
![scientific method hypothesis is scientific method hypothesis is](https://cdn.britannica.com/27/151527-050-59EE1201/variable-experiment-result-changes-others-experiments.jpg?w=300)
The scientific method is critical to the development of scientific theories , which explain empirical (experiential) laws in a scientifically rational manner. In a typical application of the scientific method, a researcher develops a hypothesis , tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments. The modified hypothesis is then retested, further modified, and tested again, until it becomes consistent with observed phenomena and testing outcomes. In this way, hypotheses serve as tools by which scientists gather data. From that data and the many different scientific investigations undertaken to explore hypotheses, scientists are able to develop broad general explanations, or scientific theories.
See also Mill’s methods ; hypothetico-deductive method .
What is the Scientific Method: How does it work and why is it important?
The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA. This ongoing approach promotes reason, evidence, and the pursuit of truth in science.
Updated on November 18, 2023
![scientific method hypothesis is What is the Scientific Method: How does it work and why is it important?](https://images.prismic.io/aje-cms-production/bf70a3ab-3dc9-4f0c-a499-cb3a34361a41_What-is-the-Scientific-Method.jpg?auto=compress,format&w=60)
Beginning in elementary school, we are exposed to the scientific method and taught how to put it into practice. As a tool for learning, it prepares children to think logically and use reasoning when seeking answers to questions.
Rather than jumping to conclusions, the scientific method gives us a recipe for exploring the world through observation and trial and error. We use it regularly, sometimes knowingly in academics or research, and sometimes subconsciously in our daily lives.
In this article we will refresh our memories on the particulars of the scientific method, discussing where it comes from, which elements comprise it, and how it is put into practice. Then, we will consider the importance of the scientific method, who uses it and under what circumstances.
What is the scientific method?
The scientific method is a dynamic process that involves objectively investigating questions through observation and experimentation . Applicable to all scientific disciplines, this systematic approach to answering questions is more accurately described as a flexible set of principles than as a fixed series of steps.
The following representations of the scientific method illustrate how it can be both condensed into broad categories and also expanded to reveal more and more details of the process. These graphics capture the adaptability that makes this concept universally valuable as it is relevant and accessible not only across age groups and educational levels but also within various contexts.
![scientific method hypothesis is a graph of the scientific method](https://images.prismic.io/aje-cms-production/5f56efcf-f422-4769-acd5-e19bb6e0991e_Scientific+Method+Broad+-+Infographic-01.jpg?auto=compress,format&rect=0,0,2480,3508&w=60)
Steps in the scientific method
While the scientific method is versatile in form and function, it encompasses a collection of principles that create a logical progression to the process of problem solving:
- Define a question : Constructing a clear and precise problem statement that identifies the main question or goal of the investigation is the first step. The wording must lend itself to experimentation by posing a question that is both testable and measurable.
- Gather information and resources : Researching the topic in question to find out what is already known and what types of related questions others are asking is the next step in this process. This background information is vital to gaining a full understanding of the subject and in determining the best design for experiments.
- Form a hypothesis : Composing a concise statement that identifies specific variables and potential results, which can then be tested, is a crucial step that must be completed before any experimentation. An imperfection in the composition of a hypothesis can result in weaknesses to the entire design of an experiment.
- Perform the experiments : Testing the hypothesis by performing replicable experiments and collecting resultant data is another fundamental step of the scientific method. By controlling some elements of an experiment while purposely manipulating others, cause and effect relationships are established.
- Analyze the data : Interpreting the experimental process and results by recognizing trends in the data is a necessary step for comprehending its meaning and supporting the conclusions. Drawing inferences through this systematic process lends substantive evidence for either supporting or rejecting the hypothesis.
- Report the results : Sharing the outcomes of an experiment, through an essay, presentation, graphic, or journal article, is often regarded as a final step in this process. Detailing the project's design, methods, and results not only promotes transparency and replicability but also adds to the body of knowledge for future research.
- Retest the hypothesis : Repeating experiments to see if a hypothesis holds up in all cases is a step that is manifested through varying scenarios. Sometimes a researcher immediately checks their own work or replicates it at a future time, or another researcher will repeat the experiments to further test the hypothesis.
![scientific method hypothesis is a chart of the scientific method](https://images.prismic.io/aje-cms-production/02cdd14e-c553-4524-8db9-928620943d70_Scientific+Method+Complex+-+Infographic-01.jpg?auto=compress,format&rect=2,0,3871,2746&w=60)
Where did the scientific method come from?
Oftentimes, ancient peoples attempted to answer questions about the unknown by:
- Making simple observations
- Discussing the possibilities with others deemed worthy of a debate
- Drawing conclusions based on dominant opinions and preexisting beliefs
For example, take Greek and Roman mythology. Myths were used to explain everything from the seasons and stars to the sun and death itself.
However, as societies began to grow through advancements in agriculture and language, ancient civilizations like Egypt and Babylonia shifted to a more rational analysis for understanding the natural world. They increasingly employed empirical methods of observation and experimentation that would one day evolve into the scientific method .
In the 4th century, Aristotle, considered the Father of Science by many, suggested these elements , which closely resemble the contemporary scientific method, as part of his approach for conducting science:
- Study what others have written about the subject.
- Look for the general consensus about the subject.
- Perform a systematic study of everything even partially related to the topic.
![scientific method hypothesis is a pyramid of the scientific method](https://images.prismic.io/aje-cms-production/d28d565c-5d38-4b23-ade5-57a4a600939b_Scientific+Method+Pyramid+-+Infographic-01.jpg?auto=compress,format&rect=3,0,6118,4116&w=60)
By continuing to emphasize systematic observation and controlled experiments, scholars such as Al-Kindi and Ibn al-Haytham helped expand this concept throughout the Islamic Golden Age .
In his 1620 treatise, Novum Organum , Sir Francis Bacon codified the scientific method, arguing not only that hypotheses must be tested through experiments but also that the results must be replicated to establish a truth. Coming at the height of the Scientific Revolution, this text made the scientific method accessible to European thinkers like Galileo and Isaac Newton who then put the method into practice.
As science modernized in the 19th century, the scientific method became more formalized, leading to significant breakthroughs in fields such as evolution and germ theory. Today, it continues to evolve, underpinning scientific progress in diverse areas like quantum mechanics, genetics, and artificial intelligence.
Why is the scientific method important?
The history of the scientific method illustrates how the concept developed out of a need to find objective answers to scientific questions by overcoming biases based on fear, religion, power, and cultural norms. This still holds true today.
By implementing this standardized approach to conducting experiments, the impacts of researchers’ personal opinions and preconceived notions are minimized. The organized manner of the scientific method prevents these and other mistakes while promoting the replicability and transparency necessary for solid scientific research.
The importance of the scientific method is best observed through its successes, for example:
- “ Albert Einstein stands out among modern physicists as the scientist who not only formulated a theory of revolutionary significance but also had the genius to reflect in a conscious and technical way on the scientific method he was using.” Devising a hypothesis based on the prevailing understanding of Newtonian physics eventually led Einstein to devise the theory of general relativity .
- Howard Florey “Perhaps the most useful lesson which has come out of the work on penicillin has been the demonstration that success in this field depends on the development and coordinated use of technical methods.” After discovering a mold that prevented the growth of Staphylococcus bacteria, Dr. Alexander Flemimg designed experiments to identify and reproduce it in the lab, thus leading to the development of penicillin .
- James D. Watson “Every time you understand something, religion becomes less likely. Only with the discovery of the double helix and the ensuing genetic revolution have we had grounds for thinking that the powers held traditionally to be the exclusive property of the gods might one day be ours. . . .” By using wire models to conceive a structure for DNA, Watson and Crick crafted a hypothesis for testing combinations of amino acids, X-ray diffraction images, and the current research in atomic physics, resulting in the discovery of DNA’s double helix structure .
Final thoughts
As the cases exemplify, the scientific method is never truly completed, but rather started and restarted. It gave these researchers a structured process that was easily replicated, modified, and built upon.
While the scientific method may “end” in one context, it never literally ends. When a hypothesis, design, methods, and experiments are revisited, the scientific method simply picks up where it left off. Each time a researcher builds upon previous knowledge, the scientific method is restored with the pieces of past efforts.
By guiding researchers towards objective results based on transparency and reproducibility, the scientific method acts as a defense against bias, superstition, and preconceived notions. As we embrace the scientific method's enduring principles, we ensure that our quest for knowledge remains firmly rooted in reason, evidence, and the pursuit of truth.
![scientific method hypothesis is The AJE Team](https://images.prismic.io/aje-cms-production/7a3dfd43-56b5-4b0e-972f-506f390a1ef2_American_Journal_Experts_author_image.jpg?auto=compress,format&w=48)
The AJE Team
See our "Privacy Policy"
Six Steps of the Scientific Method
Learn What Makes Each Stage Important
ThoughtCo. / Hugo Lin
- Scientific Method
- Chemical Laws
- Periodic Table
- Projects & Experiments
- Biochemistry
- Physical Chemistry
- Medical Chemistry
- Chemistry In Everyday Life
- Famous Chemists
- Activities for Kids
- Abbreviations & Acronyms
- Weather & Climate
- Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
- B.A., Physics and Mathematics, Hastings College
The scientific method is a systematic way of learning about the world around us. The key difference between the scientific method and other ways of acquiring knowledge is that, when using the scientific method, we make hypotheses and then test them with an experiment.
Anyone can use the scientific method to acquire knowledge by asking questions and then working to find the answers to those questions. Below are the six steps involved in the scientific method and variables you may encounter when working with this method.
The Six Steps
The number of steps in the scientific method can vary from one description to another (which mainly happens when data and analysis are separated into separate steps), however, below is a fairly standard list of the six steps you'll likely be expected to know for any science class:
- Purpose/Question Ask a question.
- Research Conduct background research. Write down your sources so you can cite your references. In the modern era, you might conduct much of your research online. As you read articles and papers online, ensure you scroll to the bottom of the text to check the author's references. Even if you can't access the full text of a published article, you can usually view the abstract to see the summary of other experiments . Interview experts on a topic. The more you know about a subject, the easier it'll be to conduct your investigation.
- Hypothesis Propose a hypothesis . This is a sort of educated guess about what you expect your research to reveal. A hypothesis is a statement used to predict the outcome of an experiment. Usually, a hypothesis is written in terms of cause and effect. Alternatively, it may describe the relationship between two phenomena. The null hypothesis or the no-difference hypothesis is one type of hypothesis that's easy to test because it assumes changing a variable will not affect the outcome. In reality, you probably expect a change, but rejecting a hypothesis may be more useful than accepting one.
- Experiment Design and experiment to test your hypothesis. An experiment has an independent and dependent variable. You change or control the independent variable and record the effect it has on the dependent variable . It's important to change only one variable for an experiment rather than try to combine the effects of variables in an experiment. For example, if you want to test the effects of light intensity and fertilizer concentration on the growth rate of a plant, you're looking at two separate experiments.
- Data/Analysis Record observations and analyze the meaning of the data. Often, you'll prepare a table or graph of the data. Don't throw out data points you think are bad or that don't support your predictions. Some of the most incredible discoveries in science were made because the data looked wrong! Once you have the data, you may need to perform a mathematical analysis to support or refute your hypothesis.
- Conclusion Conclude whether to accept or reject your hypothesis. There's no right or wrong outcome to an experiment, so either result is fine. Accepting a hypothesis doesn't necessarily mean it's correct! Sometimes repeating an experiment may give a different result. In other cases, a hypothesis may predict an outcome, yet you might draw an incorrect conclusion. Communicate your results. You can compile your results into a lab report or formally submit them as a paper . Whether you accept or reject the hypothesis, you likely learned something about the subject and may wish to revise the original hypothesis or form a new one for a future experiment.
When Are There Seven Steps?
Some teach the scientific method with seven steps instead of six. In the seven-step model, the first step is to make observations. Even if you don't make observations formally, you should think about prior experiences with a subject to ask a question or solve a problem.
Formal observations are a type of brainstorming that can help you find an idea and form a hypothesis. Observe your subject and record everything about it. Include colors, timing, sounds, temperatures, changes, behavior, and anything that strikes you as interesting or significant.
When you design an experiment, you're controlling and measuring variables. There are three types of variables:
- Controlled Variables: You can have as many controlled variables as you like. These are parts of the experiment that you try to keep constant throughout an experiment so they won't interfere with your test. Writing down controlled variables is a good idea because it helps make your experiment reproducible , which is important in science! If you have trouble duplicating results from one experiment to another, there may be a controlled variable you missed.
- Independent Variable: This is the variable you control.
- Dependent Variable: This is the variable you measure. It's called the dependent variable because it depends on the independent variable.
- Null Hypothesis Examples
- Scientific Method Flow Chart
- Random Error vs. Systematic Error
- What Is an Experimental Constant?
- Scientific Variable
- What Is a Hypothesis? (Science)
- What Are the Elements of a Good Hypothesis?
- What Are Examples of a Hypothesis?
- What Is a Testable Hypothesis?
- Scientific Hypothesis Examples
- Scientific Method Vocabulary Terms
- Understanding Simple vs Controlled Experiments
- The Role of a Controlled Variable in an Experiment
- What Is the Difference Between a Control Variable and Control Group?
- What Is a Controlled Experiment?
- DRY MIX Experiment Variables Acronym
![scientific method hypothesis is SEP home page](https://plato.stanford.edu/symbols/sep-man-red.png)
- Table of Contents
- Random Entry
- Chronological
- Editorial Information
- About the SEP
- Editorial Board
- How to Cite the SEP
- Special Characters
- Advanced Tools
- Support the SEP
- PDFs for SEP Friends
- Make a Donation
- SEPIA for Libraries
- Entry Contents
Bibliography
Academic tools.
- Friends PDF Preview
- Author and Citation Info
- Back to Top
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.
- Aikenhead, G.S., 1987, “High-school graduates’ beliefs about science-technology-society. III. Characteristics and limitations of scientific knowledge”, Science Education , 71(4): 459–487.
- Allchin, D., H.M. Andersen and K. Nielsen, 2014, “Complementary Approaches to Teaching Nature of Science: Integrating Student Inquiry, Historical Cases, and Contemporary Cases in Classroom Practice”, Science Education , 98: 461–486.
- Anderson, C., 2008, “The end of theory: The data deluge makes the scientific method obsolete”, Wired magazine , 16(7): 16–07
- Arabatzis, T., 2006, “On the inextricability of the context of discovery and the context of justification”, in Revisiting Discovery and Justification , J. Schickore and F. Steinle (eds.), Dordrecht: Springer, pp. 215–230.
- Barnes, J. (ed.), 1984, The Complete Works of Aristotle, Vols I and II , Princeton: Princeton University Press.
- Barnes, B. and D. Bloor, 1982, “Relativism, Rationalism, and the Sociology of Knowledge”, in Rationality and Relativism , M. Hollis and S. Lukes (eds.), Cambridge: MIT Press, pp. 1–20.
- Bauer, H.H., 1992, Scientific Literacy and the Myth of the Scientific Method , Urbana: University of Illinois Press.
- Bechtel, W. and R.C. Richardson, 1993, Discovering complexity , Princeton, NJ: Princeton University Press.
- Berkeley, G., 1734, The Analyst in De Motu and The Analyst: A Modern Edition with Introductions and Commentary , D. Jesseph (trans. and ed.), Dordrecht: Kluwer Academic Publishers, 1992.
- Blachowicz, J., 2009, “How science textbooks treat scientific method: A philosopher’s perspective”, The British Journal for the Philosophy of Science , 60(2): 303–344.
- Bloor, D., 1991, Knowledge and Social Imagery , Chicago: University of Chicago Press, 2 nd edition.
- Boyle, R., 1682, New experiments physico-mechanical, touching the air , Printed by Miles Flesher for Richard Davis, bookseller in Oxford.
- Bridgman, P.W., 1927, The Logic of Modern Physics , New York: Macmillan.
- –––, 1956, “The Methodological Character of Theoretical Concepts”, in The Foundations of Science and the Concepts of Science and Psychology , Herbert Feigl and Michael Scriven (eds.), Minnesota: University of Minneapolis Press, pp. 38–76.
- Burian, R., 1997, “Exploratory Experimentation and the Role of Histochemical Techniques in the Work of Jean Brachet, 1938–1952”, History and Philosophy of the Life Sciences , 19(1): 27–45.
- –––, 2007, “On microRNA and the need for exploratory experimentation in post-genomic molecular biology”, History and Philosophy of the Life Sciences , 29(3): 285–311.
- Carnap, R., 1928, Der logische Aufbau der Welt , Berlin: Bernary, transl. by R.A. George, The Logical Structure of the World , Berkeley: University of California Press, 1967.
- –––, 1956, “The methodological character of theoretical concepts”, Minnesota studies in the philosophy of science , 1: 38–76.
- Carrol, S., and D. Goodstein, 2009, “Defining the scientific method”, Nature Methods , 6: 237.
- Churchman, C.W., 1948, “Science, Pragmatics, Induction”, Philosophy of Science , 15(3): 249–268.
- Cooper, J. (ed.), 1997, Plato: Complete Works , Indianapolis: Hackett.
- Darden, L., 1991, Theory Change in Science: Strategies from Mendelian Genetics , Oxford: Oxford University Press
- Dewey, J., 1910, How we think , New York: Dover Publications (reprinted 1997).
- Douglas, H., 2009, Science, Policy, and the Value-Free Ideal , Pittsburgh: University of Pittsburgh Press.
- Dupré, J., 2004, “Miracle of Monism ”, in Naturalism in Question , Mario De Caro and David Macarthur (eds.), Cambridge, MA: Harvard University Press, pp. 36–58.
- Elliott, K.C., 2007, “Varieties of exploratory experimentation in nanotoxicology”, History and Philosophy of the Life Sciences , 29(3): 311–334.
- Elliott, K. C., and T. Richards (eds.), 2017, Exploring inductive risk: Case studies of values in science , Oxford: Oxford University Press.
- Falcon, Andrea, 2005, Aristotle and the science of nature: Unity without uniformity , Cambridge: Cambridge University Press.
- Feyerabend, P., 1978, Science in a Free Society , London: New Left Books
- –––, 1988, Against Method , London: Verso, 2 nd edition.
- Fisher, R.A., 1955, “Statistical Methods and Scientific Induction”, Journal of The Royal Statistical Society. Series B (Methodological) , 17(1): 69–78.
- Foster, K. and P.W. Huber, 1999, Judging Science. Scientific Knowledge and the Federal Courts , Cambridge: MIT Press.
- Fox Keller, E., 2003, “Models, Simulation, and ‘computer experiments’”, in The Philosophy of Scientific Experimentation , H. Radder (ed.), Pittsburgh: Pittsburgh University Press, 198–215.
- Gilbert, G., 1976, “The transformation of research findings into scientific knowledge”, Social Studies of Science , 6: 281–306.
- Gimbel, S., 2011, Exploring the Scientific Method , Chicago: University of Chicago Press.
- Goodman, N., 1965, Fact , Fiction, and Forecast , Indianapolis: Bobbs-Merrill.
- Haack, S., 1995, “Science is neither sacred nor a confidence trick”, Foundations of Science , 1(3): 323–335.
- –––, 2003, Defending science—within reason , Amherst: Prometheus.
- –––, 2005a, “Disentangling Daubert: an epistemological study in theory and practice”, Journal of Philosophy, Science and Law , 5, Haack 2005a available online . doi:10.5840/jpsl2005513
- –––, 2005b, “Trial and error: The Supreme Court’s philosophy of science”, American Journal of Public Health , 95: S66-S73.
- –––, 2010, “Federal Philosophy of Science: A Deconstruction-and a Reconstruction”, NYUJL & Liberty , 5: 394.
- Hangel, N. and J. Schickore, 2017, “Scientists’ conceptions of good research practice”, Perspectives on Science , 25(6): 766–791
- Harper, W.L., 2011, Isaac Newton’s Scientific Method: Turning Data into Evidence about Gravity and Cosmology , Oxford: Oxford University Press.
- Hempel, C., 1950, “Problems and Changes in the Empiricist Criterion of Meaning”, Revue Internationale de Philosophie , 41(11): 41–63.
- –––, 1951, “The Concept of Cognitive Significance: A Reconsideration”, Proceedings of the American Academy of Arts and Sciences , 80(1): 61–77.
- –––, 1965, Aspects of scientific explanation and other essays in the philosophy of science , New York–London: Free Press.
- –––, 1966, Philosophy of Natural Science , Englewood Cliffs: Prentice-Hall.
- Holmes, F.L., 1987, “Scientific writing and scientific discovery”, Isis , 78(2): 220–235.
- Howard, D., 2003, “Two left turns make a right: On the curious political career of North American philosophy of science at midcentury”, in Logical Empiricism in North America , G.L. Hardcastle & A.W. Richardson (eds.), Minneapolis: University of Minnesota Press, pp. 25–93.
- Hoyningen-Huene, P., 2008, “Systematicity: The nature of science”, Philosophia , 36(2): 167–180.
- –––, 2013, Systematicity. The Nature of Science , Oxford: Oxford University Press.
- Howie, D., 2002, Interpreting probability: Controversies and developments in the early twentieth century , Cambridge: Cambridge University Press.
- Hughes, R., 1999, “The Ising Model, Computer Simulation, and Universal Physics”, in Models as Mediators , M. Morgan and M. Morrison (eds.), Cambridge: Cambridge University Press, pp. 97–145
- Hume, D., 1739, A Treatise of Human Nature , D. Fate Norton and M.J. Norton (eds.), Oxford: Oxford University Press, 2000.
- Humphreys, P., 1995, “Computational science and scientific method”, Minds and Machines , 5(1): 499–512.
- ICMJE, 2013, “Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals”, International Committee of Medical Journal Editors, available online , accessed August 13 2014
- Jeffrey, R.C., 1956, “Valuation and Acceptance of Scientific Hypotheses”, Philosophy of Science , 23(3): 237–246.
- Kaufmann, W.J., and L.L. Smarr, 1993, Supercomputing and the Transformation of Science , New York: Scientific American Library.
- Knorr-Cetina, K., 1981, The Manufacture of Knowledge , Oxford: Pergamon Press.
- Krohs, U., 2012, “Convenience experimentation”, Studies in History and Philosophy of Biological and BiomedicalSciences , 43: 52–57.
- Kuhn, T.S., 1962, The Structure of Scientific Revolutions , Chicago: University of Chicago Press
- Latour, B. and S. Woolgar, 1986, Laboratory Life: The Construction of Scientific Facts , Princeton: Princeton University Press, 2 nd edition.
- Laudan, L., 1968, “Theories of scientific method from Plato to Mach”, History of Science , 7(1): 1–63.
- Lenhard, J., 2006, “Models and statistical inference: The controversy between Fisher and Neyman-Pearson”, The British Journal for the Philosophy of Science , 57(1): 69–91.
- Leonelli, S., 2012, “Making Sense of Data-Driven Research in the Biological and the Biomedical Sciences”, Studies in the History and Philosophy of the Biological and Biomedical Sciences , 43(1): 1–3.
- Levi, I., 1960, “Must the scientist make value judgments?”, Philosophy of Science , 57(11): 345–357
- Lindley, D., 1991, Theory Change in Science: Strategies from Mendelian Genetics , Oxford: Oxford University Press.
- Lipton, P., 2004, Inference to the Best Explanation , London: Routledge, 2 nd edition.
- Marks, H.M., 2000, The progress of experiment: science and therapeutic reform in the United States, 1900–1990 , Cambridge: Cambridge University Press.
- Mazzochi, F., 2015, “Could Big Data be the end of theory in science?”, EMBO reports , 16: 1250–1255.
- Mayo, D.G., 1996, Error and the Growth of Experimental Knowledge , Chicago: University of Chicago Press.
- McComas, W.F., 1996, “Ten myths of science: Reexamining what we think we know about the nature of science”, School Science and Mathematics , 96(1): 10–16.
- Medawar, P.B., 1963/1996, “Is the scientific paper a fraud”, in The Strange Case of the Spotted Mouse and Other Classic Essays on Science , Oxford: Oxford University Press, 33–39.
- Mill, J.S., 1963, Collected Works of John Stuart Mill , J. M. Robson (ed.), Toronto: University of Toronto Press
- NAS, 1992, Responsible Science: Ensuring the integrity of the research process , Washington DC: National Academy Press.
- Nersessian, N.J., 1987, “A cognitive-historical approach to meaning in scientific theories”, in The process of science , N. Nersessian (ed.), Berlin: Springer, pp. 161–177.
- –––, 2008, Creating Scientific Concepts , Cambridge: MIT Press.
- Newton, I., 1726, Philosophiae naturalis Principia Mathematica (3 rd edition), in The Principia: Mathematical Principles of Natural Philosophy: A New Translation , I.B. Cohen and A. Whitman (trans.), Berkeley: University of California Press, 1999.
- –––, 1704, Opticks or A Treatise of the Reflections, Refractions, Inflections & Colors of Light , New York: Dover Publications, 1952.
- Neyman, J., 1956, “Note on an Article by Sir Ronald Fisher”, Journal of the Royal Statistical Society. Series B (Methodological) , 18: 288–294.
- Nickles, T., 1987, “Methodology, heuristics, and rationality”, in Rational changes in science: Essays on Scientific Reasoning , J.C. Pitt (ed.), Berlin: Springer, pp. 103–132.
- Nicod, J., 1924, Le problème logique de l’induction , Paris: Alcan. (Engl. transl. “The Logical Problem of Induction”, in Foundations of Geometry and Induction , London: Routledge, 2000.)
- Nola, R. and H. Sankey, 2000a, “A selective survey of theories of scientific method”, in Nola and Sankey 2000b: 1–65.
- –––, 2000b, After Popper, Kuhn and Feyerabend. Recent Issues in Theories of Scientific Method , London: Springer.
- –––, 2007, Theories of Scientific Method , Stocksfield: Acumen.
- Norton, S., and F. Suppe, 2001, “Why atmospheric modeling is good science”, in Changing the Atmosphere: Expert Knowledge and Environmental Governance , C. Miller and P. Edwards (eds.), Cambridge, MA: MIT Press, 88–133.
- O’Malley, M., 2007, “Exploratory experimentation and scientific practice: Metagenomics and the proteorhodopsin case”, History and Philosophy of the Life Sciences , 29(3): 337–360.
- O’Malley, M., C. Haufe, K. Elliot, and R. Burian, 2009, “Philosophies of Funding”, Cell , 138: 611–615.
- Oreskes, N., K. Shrader-Frechette, and K. Belitz, 1994, “Verification, Validation and Confirmation of Numerical Models in the Earth Sciences”, Science , 263(5147): 641–646.
- Osborne, J., S. Simon, and S. Collins, 2003, “Attitudes towards science: a review of the literature and its implications”, International Journal of Science Education , 25(9): 1049–1079.
- Parascandola, M., 1998, “Epidemiology—2 nd -Rate Science”, Public Health Reports , 113(4): 312–320.
- Parker, W., 2008a, “Franklin, Holmes and the Epistemology of Computer Simulation”, International Studies in the Philosophy of Science , 22(2): 165–83.
- –––, 2008b, “Computer Simulation through an Error-Statistical Lens”, Synthese , 163(3): 371–84.
- Pearson, K. 1892, The Grammar of Science , London: J.M. Dents and Sons, 1951
- Pearson, E.S., 1955, “Statistical Concepts in Their Relation to Reality”, Journal of the Royal Statistical Society , B, 17: 204–207.
- Pickering, A., 1984, Constructing Quarks: A Sociological History of Particle Physics , Edinburgh: Edinburgh University Press.
- Popper, K.R., 1959, The Logic of Scientific Discovery , London: Routledge, 2002
- –––, 1963, Conjectures and Refutations , London: Routledge, 2002.
- –––, 1985, Unended Quest: An Intellectual Autobiography , La Salle: Open Court Publishing Co..
- Rudner, R., 1953, “The Scientist Qua Scientist Making Value Judgments”, Philosophy of Science , 20(1): 1–6.
- Rudolph, J.L., 2005, “Epistemology for the masses: The origin of ‘The Scientific Method’ in American Schools”, History of Education Quarterly , 45(3): 341–376
- Schickore, J., 2008, “Doing science, writing science”, Philosophy of Science , 75: 323–343.
- Schickore, J. and N. Hangel, 2019, “‘It might be this, it should be that…’ uncertainty and doubt in day-to-day science practice”, European Journal for Philosophy of Science , 9(2): 31. doi:10.1007/s13194-019-0253-9
- Shamoo, A.E. and D.B. Resnik, 2009, Responsible Conduct of Research , Oxford: Oxford University Press.
- Shank, J.B., 2008, The Newton Wars and the Beginning of the French Enlightenment , Chicago: The University of Chicago Press.
- Shapin, S. and S. Schaffer, 1985, Leviathan and the air-pump , Princeton: Princeton University Press.
- Smith, G.E., 2002, “The Methodology of the Principia”, in The Cambridge Companion to Newton , I.B. Cohen and G.E. Smith (eds.), Cambridge: Cambridge University Press, 138–173.
- Snyder, L.J., 1997a, “Discoverers’ Induction”, Philosophy of Science , 64: 580–604.
- –––, 1997b, “The Mill-Whewell Debate: Much Ado About Induction”, Perspectives on Science , 5: 159–198.
- –––, 1999, “Renovating the Novum Organum: Bacon, Whewell and Induction”, Studies in History and Philosophy of Science , 30: 531–557.
- Sober, E., 2008, Evidence and Evolution. The logic behind the science , Cambridge: Cambridge University Press
- Sprenger, J. and S. Hartmann, 2019, Bayesian philosophy of science , Oxford: Oxford University Press.
- Steinle, F., 1997, “Entering New Fields: Exploratory Uses of Experimentation”, Philosophy of Science (Proceedings), 64: S65–S74.
- –––, 2002, “Experiments in History and Philosophy of Science”, Perspectives on Science , 10(4): 408–432.
- Strasser, B.J., 2012, “Data-driven sciences: From wonder cabinets to electronic databases”, Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 43(1): 85–87.
- Succi, S. and P.V. Coveney, 2018, “Big data: the end of the scientific method?”, Philosophical Transactions of the Royal Society A , 377: 20180145. doi:10.1098/rsta.2018.0145
- Suppe, F., 1998, “The Structure of a Scientific Paper”, Philosophy of Science , 65(3): 381–405.
- Swijtink, Z.G., 1987, “The objectification of observation: Measurement and statistical methods in the nineteenth century”, in The probabilistic revolution. Ideas in History, Vol. 1 , L. Kruger (ed.), Cambridge MA: MIT Press, pp. 261–285.
- Waters, C.K., 2007, “The nature and context of exploratory experimentation: An introduction to three case studies of exploratory research”, History and Philosophy of the Life Sciences , 29(3): 275–284.
- Weinberg, S., 1995, “The methods of science… and those by which we live”, Academic Questions , 8(2): 7–13.
- Weissert, T., 1997, The Genesis of Simulation in Dynamics: Pursuing the Fermi-Pasta-Ulam Problem , New York: Springer Verlag.
- William H., 1628, Exercitatio Anatomica de Motu Cordis et Sanguinis in Animalibus , in On the Motion of the Heart and Blood in Animals , R. Willis (trans.), Buffalo: Prometheus Books, 1993.
- Winsberg, E., 2010, Science in the Age of Computer Simulation , Chicago: University of Chicago Press.
- Wivagg, D. & D. Allchin, 2002, “The Dogma of the Scientific Method”, The American Biology Teacher , 64(9): 645–646
How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.
- Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
- Scientific Method at philpapers. Darrell Rowbottom (ed.).
- Recent Articles | Scientific Method | The Scientist Magazine
al-Kindi | Albert the Great [= Albertus magnus] | Aquinas, Thomas | Arabic and Islamic Philosophy, disciplines in: natural philosophy and natural science | Arabic and Islamic Philosophy, historical and methodological topics in: Greek sources | Arabic and Islamic Philosophy, historical and methodological topics in: influence of Arabic and Islamic Philosophy on the Latin West | Aristotle | Bacon, Francis | Bacon, Roger | Berkeley, George | biology: experiment in | Boyle, Robert | Cambridge Platonists | confirmation | Descartes, René | Enlightenment | epistemology | epistemology: Bayesian | epistemology: social | Feyerabend, Paul | Galileo Galilei | Grosseteste, Robert | Hempel, Carl | Hume, David | Hume, David: Newtonianism and Anti-Newtonianism | induction: problem of | Kant, Immanuel | Kuhn, Thomas | Leibniz, Gottfried Wilhelm | Locke, John | Mill, John Stuart | More, Henry | Neurath, Otto | Newton, Isaac | Newton, Isaac: philosophy | Ockham [Occam], William | operationalism | Peirce, Charles Sanders | Plato | Popper, Karl | rationality: historicist theories of | Reichenbach, Hans | reproducibility, scientific | Schlick, Moritz | science: and pseudo-science | science: theory and observation in | science: unity of | scientific discovery | scientific knowledge: social dimensions of | simulations in science | skepticism: medieval | space and time: absolute and relational space and motion, post-Newtonian theories | Vienna Circle | Whewell, William | Zabarella, Giacomo
Copyright © 2021 by Brian Hepburn < brian . hepburn @ wichita . edu > Hanne Andersen < hanne . andersen @ ind . ku . dk >
- Accessibility
Support SEP
Mirror sites.
View this site from another server:
- Info about mirror sites
The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University
Library of Congress Catalog Data: ISSN 1095-5054
What Are The Steps Of The Scientific Method?
Julia Simkus
Editor at Simply Psychology
BA (Hons) Psychology, Princeton University
Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.
Learn about our Editorial Process
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
Science is not just knowledge. It is also a method for obtaining knowledge. Scientific understanding is organized into theories.
The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.
It involves careful observation, asking questions, formulating hypotheses, experimental testing, and refining hypotheses based on experimental findings.
How it is Used
The scientific method can be applied broadly in science across many different fields, such as chemistry, physics, geology, and psychology. In a typical application of this process, a researcher will develop a hypothesis, test this hypothesis, and then modify the hypothesis based on the outcomes of the experiment.
The process is then repeated with the modified hypothesis until the results align with the observed phenomena. Detailed steps of the scientific method are described below.
Keep in mind that the scientific method does not have to follow this fixed sequence of steps; rather, these steps represent a set of general principles or guidelines.
7 Steps of the Scientific Method
Psychology uses an empirical approach.
Empiricism (founded by John Locke) states that the only source of knowledge comes through our senses – e.g., sight, hearing, touch, etc.
Empirical evidence does not rely on argument or belief. Thus, empiricism is the view that all knowledge is based on or may come from direct observation and experience.
The empiricist approach of gaining knowledge through experience quickly became the scientific approach and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.
![What Are The Steps Of The Scientific Method? 1 Steps of the Scientific Method](https://www.simplypsychology.org/wp-content/uploads/scientific-method.jpg)
Step 1: Make an Observation (Theory Construction)
Every researcher starts at the very beginning. Before diving in and exploring something, one must first determine what they will study – it seems simple enough!
By making observations, researchers can establish an area of interest. Once this topic of study has been chosen, a researcher should review existing literature to gain insight into what has already been tested and determine what questions remain unanswered.
This assessment will provide helpful information about what has already been comprehended about the specific topic and what questions remain, and if one can go and answer them.
Specifically, a literature review might implicate examining a substantial amount of documented material from academic journals to books dating back decades. The most appropriate information gathered by the researcher will be shown in the introduction section or abstract of the published study results.
The background material and knowledge will help the researcher with the first significant step in conducting a psychology study, which is formulating a research question.
This is the inductive phase of the scientific process. Observations yield information that is used to formulate theories as explanations. A theory is a well-developed set of ideas that propose an explanation for observed phenomena.
Inductive reasoning moves from specific premises to a general conclusion. It starts with observations of phenomena in the natural world and derives a general law.
Step 2: Ask a Question
Once a researcher has made observations and conducted background research, the next step is to ask a scientific question. A scientific question must be defined, testable, and measurable.
A useful approach to develop a scientific question is: “What is the effect of…?” or “How does X affect Y?”
To answer an experimental question, a researcher must identify two variables: the independent and dependent variables.
The independent variable is the variable manipulated (the cause), and the dependent variable is the variable being measured (the effect).
An example of a research question could be, “Is handwriting or typing more effective for retaining information?” Answering the research question and proposing a relationship between the two variables is discussed in the next step.
Step 3: Form a Hypothesis (Make Predictions)
A hypothesis is an educated guess about the relationship between two or more variables. A hypothesis is an attempt to answer your research question based on prior observation and background research. Theories tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.
For example, a researcher might ask about the connection between sleep and educational performance. Do students who get less sleep perform worse on tests at school?
It is crucial to think about different questions one might have about a particular topic to formulate a reasonable hypothesis. It would help if one also considered how one could investigate the causalities.
It is important that the hypothesis is both testable against reality and falsifiable. This means that it can be tested through an experiment and can be proven wrong.
The falsification principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.
To test a hypothesis, we first assume that there is no difference between the populations from which the samples were taken. This is known as the null hypothesis and predicts that the independent variable will not influence the dependent variable.
Examples of “if…then…” Hypotheses:
- If one gets less than 6 hours of sleep, then one will do worse on tests than if one obtains more rest.
- If one drinks lots of water before going to bed, one will have to use the bathroom often at night.
- If one practices exercising and lighting weights, then one’s body will begin to build muscle.
The research hypothesis is often called the alternative hypothesis and predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.
It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.
Although one could state and write a scientific hypothesis in many ways, hypotheses are usually built like “if…then…” statements.
Step 4: Run an Experiment (Gather Data)
The next step in the scientific method is to test your hypothesis and collect data. A researcher will design an experiment to test the hypothesis and gather data that will either support or refute the hypothesis.
The exact research methods used to examine a hypothesis depend on what is being studied. A psychologist might utilize two primary forms of research, experimental research, and descriptive research.
The scientific method is objective in that researchers do not let preconceived ideas or biases influence the collection of data and is systematic in that experiments are conducted in a logical way.
Experimental Research
Experimental research is used to investigate cause-and-effect associations between two or more variables. This type of research systematically controls an independent variable and measures its effect on a specified dependent variable.
Experimental research involves manipulating an independent variable and measuring the effect(s) on the dependent variable. Repeating the experiment multiple times is important to confirm that your results are accurate and consistent.
One of the significant advantages of this method is that it permits researchers to determine if changes in one variable cause shifts in each other.
While experiments in psychology typically have many moving parts (and can be relatively complex), an easy investigation is rather fundamental. Still, it does allow researchers to specify cause-and-effect associations between variables.
Most simple experiments use a control group, which involves those who do not receive the treatment, and an experimental group, which involves those who do receive the treatment.
An example of experimental research would be when a pharmaceutical company wants to test a new drug. They give one group a placebo (control group) and the other the actual pill (experimental group).
Descriptive Research
Descriptive research is generally used when it is challenging or even impossible to control the variables in question. Examples of descriptive analysis include naturalistic observation, case studies , and correlation studies .
One example of descriptive research includes phone surveys that marketers often use. While they typically do not allow researchers to identify cause and effect, correlational studies are quite common in psychology research. They make it possible to spot associations between distinct variables and measure the solidity of those relationships.
Step 5: Analyze the Data and Draw Conclusions
Once a researcher has designed and done the investigation and collected sufficient data, it is time to inspect this gathered information and judge what has been found. Researchers can summarize the data, interpret the results, and draw conclusions based on this evidence using analyses and statistics.
Upon completion of the experiment, you can collect your measurements and analyze the data using statistics. Based on the outcomes, you will either reject or confirm your hypothesis.
Analyze the Data
So, how does a researcher determine what the results of their study mean? Statistical analysis can either support or refute a researcher’s hypothesis and can also be used to determine if the conclusions are statistically significant.
When outcomes are said to be “statistically significant,” it is improbable that these results are due to luck or chance. Based on these observations, investigators must then determine what the results mean.
An experiment will support a hypothesis in some circumstances, but sometimes it fails to be truthful in other cases.
What occurs if the developments of a psychology investigation do not endorse the researcher’s hypothesis? It does mean that the study was worthless. Simply because the findings fail to defend the researcher’s hypothesis does not mean that the examination is not helpful or instructive.
This kind of research plays a vital role in supporting scientists in developing unexplored questions and hypotheses to investigate in the future. After decisions have been made, the next step is to communicate the results with the rest of the scientific community.
This is an integral part of the process because it contributes to the general knowledge base and can assist other scientists in finding new research routes to explore.
If the hypothesis is not supported, a researcher should acknowledge the experiment’s results, formulate a new hypothesis, and develop a new experiment.
We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist that could refute a theory.
Draw Conclusions and Interpret the Data
When the empirical observations disagree with the hypothesis, a number of possibilities must be considered. It might be that the theory is incorrect, in which case it needs altering, so it fully explains the data.
Alternatively, it might be that the hypothesis was poorly derived from the original theory, in which case the scientists were expecting the wrong thing to happen.
It might also be that the research was poorly conducted, or used an inappropriate method, or there were factors in play that the researchers did not consider. This will begin the process of the scientific method again.
If the hypothesis is supported, the researcher can find more evidence to support their hypothesis or look for counter-evidence to strengthen their hypothesis further.
In either scenario, the researcher should share their results with the greater scientific community.
Step 6: Share Your Results
One of the final stages of the research cycle involves the publication of the research. Once the report is written, the researcher(s) may submit the work for publication in an appropriate journal.
Usually, this is done by writing up a study description and publishing the article in a professional or academic journal. The studies and conclusions of psychological work can be seen in peer-reviewed journals such as Developmental Psychology , Psychological Bulletin, the Journal of Social Psychology, and numerous others.
Scientists should report their findings by writing up a description of their study and any subsequent findings. This enables other researchers to build upon the present research or replicate the results.
As outlined by the American Psychological Association (APA), there is a typical structure of a journal article that follows a specified format. In these articles, researchers:
- Supply a brief narrative and background on previous research
- Give their hypothesis
- Specify who participated in the study and how they were chosen
- Provide operational definitions for each variable
- Explain the measures and methods used to collect data
- Describe how the data collected was interpreted
- Discuss what the outcomes mean
A detailed record of psychological studies and all scientific studies is vital to clearly explain the steps and procedures used throughout the study. So that other researchers can try this experiment too and replicate the results.
The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound. Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.
This last step is important because all results, whether they supported or did not support the hypothesis, can contribute to the scientific community. Publication of empirical observations leads to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular.
The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound.
Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.
By replicating studies, psychologists can reduce errors, validate theories, and gain a stronger understanding of a particular topic.
Step 7: Repeat the Scientific Method (Iteration)
Now, if one’s hypothesis turns out to be accurate, find more evidence or find counter-evidence. If one’s hypothesis is false, create a new hypothesis or try again.
One may wish to revise their first hypothesis to make a more niche experiment to design or a different specific question to test.
The amazingness of the scientific method is that it is a comprehensive and straightforward process that scientists, and everyone, can utilize over and over again.
So, draw conclusions and repeat because the scientific method is never-ending, and no result is ever considered perfect.
The scientific method is a process of:
- Making an observation.
- Forming a hypothesis.
- Making a prediction.
- Experimenting to test the hypothesis.
The procedure of repeating the scientific method is crucial to science and all fields of human knowledge.
![](http://blog10.website/777/templates/cheerup2/res/banner1.gif)
Further Information
- Karl Popper – Falsification
- Thomas – Kuhn Paradigm Shift
- Positivism in Sociology: Definition, Theory & Examples
- Is Psychology a Science?
- Psychology as a Science (PDF)
List the 6 steps of the scientific methods in order
- Make an observation (theory construction)
- Ask a question. A scientific question must be defined, testable, and measurable.
- Form a hypothesis (make predictions)
- Run an experiment to test the hypothesis (gather data)
- Analyze the data and draw conclusions
- Share your results so that other researchers can make new hypotheses
What is the first step of the scientific method?
The first step of the scientific method is making an observation. This involves noticing and describing a phenomenon or group of phenomena that one finds interesting and wishes to explain.
Observations can occur in a natural setting or within the confines of a laboratory. The key point is that the observation provides the initial question or problem that the rest of the scientific method seeks to answer or solve.
What is the scientific method?
The scientific method is a step-by-step process that investigators can follow to determine if there is a causal connection between two or more variables.
Psychologists and other scientists regularly suggest motivations for human behavior. On a more casual level, people judge other people’s intentions, incentives, and actions daily.
While our standard assessments of human behavior are subjective and anecdotal, researchers use the scientific method to study psychology objectively and systematically.
All utilize a scientific method to study distinct aspects of people’s thinking and behavior. This process allows scientists to analyze and understand various psychological phenomena, but it also provides investigators and others a way to disseminate and debate the results of their studies.
The outcomes of these studies are often noted in popular media, which leads numerous to think about how or why researchers came to the findings they did.
Why Use the Six Steps of the Scientific Method
The goal of scientists is to understand better the world that surrounds us. Scientific research is the most critical tool for navigating and learning about our complex world.
Without it, we would be compelled to rely solely on intuition, other people’s power, and luck. We can eliminate our preconceived concepts and superstitions through methodical scientific research and gain an objective sense of ourselves and our world.
All psychological studies aim to explain, predict, and even control or impact mental behaviors or processes. So, psychologists use and repeat the scientific method (and its six steps) to perform and record essential psychological research.
So, psychologists focus on understanding behavior and the cognitive (mental) and physiological (body) processes underlying behavior.
In the real world, people use to understand the behavior of others, such as intuition and personal experience. The hallmark of scientific research is evidence to support a claim.
Scientific knowledge is empirical, meaning it is grounded in objective, tangible evidence that can be observed repeatedly, regardless of who is watching.
The scientific method is crucial because it minimizes the impact of bias or prejudice on the experimenter. Regardless of how hard one tries, even the best-intentioned scientists can’t escape discrimination. can’t
It stems from personal opinions and cultural beliefs, meaning any mortal filters data based on one’s experience. Sadly, this “filtering” process can cause a scientist to favor one outcome over another.
For an everyday person trying to solve a minor issue at home or work, succumbing to these biases is not such a big deal; in fact, most times, it is important.
But in the scientific community, where results must be inspected and reproduced, bias or discrimination must be avoided.
When to Use the Six Steps of the Scientific Method ?
One can use the scientific method anytime, anywhere! From the smallest conundrum to solving global problems, it is a process that can be applied to any science and any investigation.
Even if you are not considered a “scientist,” you will be surprised to know that people of all disciplines use it for all kinds of dilemmas.
Try to catch yourself next time you come by a question and see how you subconsciously or consciously use the scientific method.
![What Are The Steps Of The Scientific Method? 2 Print Friendly, PDF & Email](https://www.simplypsychology.org/wp-content/uploads/three-icons-with-text-light-blue.jpg)
- Bipolar Disorder
- Therapy Center
- When To See a Therapist
- Types of Therapy
- Best Online Therapy
- Best Couples Therapy
- Managing Stress
- Sleep and Dreaming
- Understanding Emotions
- Self-Improvement
- Healthy Relationships
- Student Resources
- Personality Types
- Sweepstakes
- Guided Meditations
- Verywell Mind Insights
- 2024 Verywell Mind 25
- Mental Health in the Classroom
- Editorial Process
- Meet Our Review Board
- Crisis Support
Scientific Method Steps in Psychology Research
Steps, Uses, and Key Terms
Verywell / Theresa Chiechi
How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave.
When conducting research, the scientific method steps to follow are:
- Observe what you want to investigate
- Ask a research question and make predictions
- Test the hypothesis and collect data
- Examine the results and draw conclusions
- Report and share the results
This process not only allows scientists to investigate and understand different psychological phenomena but also provides researchers and others a way to share and discuss the results of their studies.
Generally, there are five main steps in the scientific method, although some may break down this process into six or seven steps. An additional step in the process can also include developing new research questions based on your findings.
What Is the Scientific Method?
What is the scientific method and how is it used in psychology?
The scientific method consists of five steps. It is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.
By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.
Scientific Method Steps
While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.
The following are the scientific method steps:
Step 1. Make an Observation
Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.
A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades.
The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study: formulating a hypothesis.
Step 2. Ask a Question
Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables
For example, a researcher might ask a question about the relationship between sleep and academic performance: Do students who get more sleep perform better on tests at school?
In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic.
You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.
Step 3. Test Your Hypothesis and Collect Data
Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize: descriptive research or experimental research.
Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation , and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.
Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships.
Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable .
One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.
While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).
Step 4. Examine the Results and Draw Conclusions
Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found. Using statistics , researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.
So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant.
When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.
Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.
So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless?
Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.
After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.
Step 5. Report the Results
The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as Psychological Bulletin , the Journal of Social Psychology , Developmental Psychology , and many others.
The structure of a journal article follows a specified format that has been outlined by the American Psychological Association (APA) . In these articles, researchers:
- Provide a brief history and background on previous research
- Present their hypothesis
- Identify who participated in the study and how they were selected
- Provide operational definitions for each variable
- Describe the measures and procedures that were used to collect data
- Explain how the information collected was analyzed
- Discuss what the results mean
Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.
Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.
Before you begin exploring the scientific method steps, here's a review of some key terms and definitions that you should be familiar with:
- Falsifiable : The variables can be measured so that if a hypothesis is false, it can be proven false
- Hypothesis : An educated guess about the possible relationship between two or more variables
- Variable : A factor or element that can change in observable and measurable ways
- Operational definition : A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured
Uses for the Scientific Method
The goals of psychological studies are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.
Goals of Scientific Research in Psychology
Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.
Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations , and actions of others on a daily basis.
While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.
Examples of the Scientific Method
Now that you're familiar with the scientific method steps, it's useful to see how each step could work with a real-life example.
Say, for instance, that researchers set out to discover what the relationship is between psychotherapy and anxiety .
- Step 1. Make an observation : The researchers choose to focus their study on adults ages 25 to 40 with generalized anxiety disorder.
- Step 2. Ask a question : The question they want to answer in their study is: Do weekly psychotherapy sessions reduce symptoms in adults ages 25 to 40 with generalized anxiety disorder?
- Step 3. Test your hypothesis : Researchers collect data on participants' anxiety symptoms . They work with therapists to create a consistent program that all participants undergo. Group 1 may attend therapy once per week, whereas group 2 does not attend therapy.
- Step 4. Examine the results : Participants record their symptoms and any changes over a period of three months. After this period, people in group 1 report significant improvements in their anxiety symptoms, whereas those in group 2 report no significant changes.
- Step 5. Report the results : Researchers write a report that includes their hypothesis, information on participants, variables, procedure, and conclusions drawn from the study. In this case, they say that "Weekly therapy sessions are shown to reduce anxiety symptoms in adults ages 25 to 40."
Of course, there are many details that go into planning and executing a study such as this. But this general outline gives you an idea of how an idea is formulated and tested, and how researchers arrive at results using the scientific method.
Erol A. How to conduct scientific research ? Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102
University of Minnesota. Psychologists use the scientific method to guide their research .
Shaughnessy, JJ, Zechmeister, EB, & Zechmeister, JS. Research Methods In Psychology . New York: McGraw Hill Education; 2015.
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
- A to Z Guides
What Is the Scientific Method?
![scientific method hypothesis is scientific method hypothesis is](https://img.lb.wbmdstatic.com/vim/live/webmd/consumer_assets/site_images/articles/biographies/sheikh_zilpah_382x382.jpg?resize=51px:51px&output-quality=75)
The scientific method is a systematic way of conducting experiments or studies so that you can explore the things you observe in the world and answer questions about them. The scientific method, also known as the hypothetico-deductive method, is a series of steps that can help you accurately describe the things you observe or improve your understanding of them.
Ultimately, your goal when you use the scientific method is to:
- Find a cause-and-effect relationship by asking a question about something you observed
- Collect as much evidence as you can about what you observed, as this can help you explore the connection between your evidence and what you observed
- Determine if all your evidence can be combined to answer your question in a way that makes sense
Francis Bacon and René Descartes are usually credited with formalizing the process in the 16th and 17th centuries. The two philosophers argued that research shouldn’t be guided by preset metaphysical ideas of how reality works. They supported the use of inductive reasoning to come up with hypotheses and understand new things about reality.
Scientific Method Steps
The scientific method is a step-by-step problem-solving process. These steps include:
Observe the world around you. This will help you come up with a topic you are interested in and want to learn more about. In many cases, you already have a topic in mind because you have a related question for which you couldn't find an immediate answer.
Either way, you'll start the process by finding out what people before you already know about the topic, as well as any questions that people are still asking about. You may need to look up and read books and articles from academic journals or talk to other people so that you understand as much as you possibly can about your topic. This will help you with your next step.
Ask questions. Asking questions about what you observed and learned from reading and talking to others can help you figure out what the "problem" is. Scientists try to ask questions that are both interesting and specific and can be answered with the help of a fairly easy experiment or series of experiments. Your question should have one part (called a variable) that you can change in your experiment and another variable that you can measure. Your goal is to design an experiment that is a "fair test," which is when all the conditions in the experiment are kept the same except for the one you change (called the experimental or independent variable).
Form a hypothesis and make predictions based on it. A hypothesis is an educated guess about the relationship between two or more variables in your question. A good hypothesis lets you predict what will happen when you test it in an experiment. Another important feature of a good hypothesis is that, if the hypothesis is wrong, you should be able to show that it's wrong. This is called falsifiability. If your experiment shows that your prediction is true, then your hypothesis is supported by your data.
Test your prediction by doing an experiment or making more observations. The way you test your prediction depends on what you are studying. The best support comes from an experiment, but in some cases, it's too hard or impossible to change the variables in an experiment. Sometimes, you may need to do descriptive research where you gather more observations instead of doing an experiment. You will carefully gather notes and measurements during your experiments or studies, and you can share them with other people interested in the same question as you. Ideally, you will also repeat your experiment a couple more times because it's possible to get a result by chance, but it's less possible to get the same result more than once by chance.
Draw a conclusion. You will analyze what you already know about your topic from your literature research and the data gathered during your experiment. This will help you decide if the conclusion you draw from your data supports or contradicts your hypothesis. If your results contradict your hypothesis, you can use this observation to form a new hypothesis and make a new prediction. This is why scientific research is ongoing and scientific knowledge is changing all the time. It's very common for scientists to get results that don't support their hypotheses. In fact, you sometimes learn more about the world when your experiments don't support your hypotheses because it leads you to ask more questions. And this time around, you already know that one possible explanation is likely wrong.
Use your results to guide your next steps (iterate). For instance, if your hypothesis is supported, you may do more experiments to confirm it. Or you could come up with a hypothesis about why it works this way and design an experiment to test that. If your hypothesis is not supported, you can come up with another hypothesis and do experiments to test it. You'll rarely get the right hypothesis in one go. Most of the time, you'll have to go back to the hypothesis stage and try again. Every attempt offers you important information that helps you improve your next round of questions, hypotheses, and predictions.
Share your results. Scientific research isn't something you can do on your own; you must work with other people to do it. You may be able to do an experiment or a series of experiments on your own, but you can't come up with all the ideas or do all the experiments by yourself .
Scientists and researchers usually share information by publishing it in a scientific journal or by presenting it to their colleagues during meetings and scientific conferences. These journals are read and the conferences are attended by other researchers who are interested in the same questions. If there's anything wrong with your hypothesis, prediction, experiment design, or conclusion, other researchers will likely find it and point it out to you.
It can be scary, but it's a critical part of doing scientific research. You must let your research be examined by other researchers who are as interested and knowledgeable about your question as you. This process helps other researchers by pointing out hypotheses that have been proved wrong and why they are wrong. It helps you by identifying flaws in your thinking or experiment design. And if you don't share what you've learned and let other people ask questions about it, it's not helpful to your or anyone else's understanding of what happens in the world.
Scientific Method Example
Here's an everyday example of how you can apply the scientific method to understand more about your world so you can solve your problems in a helpful way.
Let's say you put slices of bread in your toaster and press the button, but nothing happens. Your toaster isn't working, but you can't afford to buy a new one right now. You might be able to rescue it from the trash can if you can figure out what's wrong with it. So, let's figure out what's wrong with your toaster.
Observation. Your toaster isn't working to toast your bread.
Ask a question. In this case, you're asking, "Why isn't my toaster working?" You could even do a bit of preliminary research by looking in the owner's manual for your toaster. The manufacturer has likely tested your toaster model under many conditions, and they may have some ideas for where to start with your hypothesis.
Form a hypothesis and make predictions based on it. Your hypothesis should be a potential explanation or answer to the question that you can test to see if it's correct. One possible explanation that we could test is that the power outlet is broken. Our prediction is that if the outlet is broken, then plugging it into a different outlet should make the toaster work again.
Test your prediction by doing an experiment or making more observations. You plug the toaster into a different outlet and try to toast your bread.
If that works, then your hypothesis is supported by your experimental data. Results that support your hypothesis don't prove it right; they simply suggest that it's a likely explanation. This uncertainty arises because, in the real world, we can't rule out the possibility of mistakes, wrong assumptions, or weird coincidences affecting the results. If the toaster doesn’t work even after plugging it into a different outlet, then your hypothesis is not supported and it's likely the wrong explanation.
Use your results to guide your next steps (iteration). If your toaster worked, you may decide to do further tests to confirm it or revise it. For example, you could plug something else that you know is working into the first outlet to see if that stops working too. That would be further confirmation that your hypothesis is correct.
If your toaster failed to toast when plugged into the second outlet, you need a new hypothesis. For example, your next hypothesis might be that the toaster has a shorted wire. You could test this hypothesis directly if you have the right equipment and training, or you could take it to a repair shop where they could test that hypothesis for you.
Share your results. For this everyday example, you probably wouldn't want to write a paper, but you could share your problem-solving efforts with your housemates or anyone you hire to repair your outlet or help you test if the toaster has a short circuit.
What the Scientific Method Is Used For
The scientific method is useful whenever you need to reason logically about your questions and gather evidence to support your problem-solving efforts. So, you can use it in everyday life to answer many of your questions; however, when most people think of the scientific method, they likely think of using it to:
Describe how nature works . It can be hard to accurately describe how nature works because it's almost impossible to account for every variable that's involved in a natural process. Researchers may not even know about many of the variables that are involved. In some cases, all you can do is make assumptions. But you can use the scientific method to logically disprove wrong assumptions by identifying flaws in the reasoning.
Do scientific research in a laboratory to develop things such as new medicines.
Develop critical thinking skills. Using the scientific method may help you develop critical thinking in your daily life because you learn to systematically ask questions and gather evidence to find answers. Without logical reasoning, you might be more likely to have a distorted perspective or bias. Bias is the inclination we all have to favor one perspective (usually our own) over another.
The scientific method doesn't perfectly solve the problem of bias, but it does make it harder for an entire field to be biased in the same direction. That's because it's unlikely that all the people working in a field have the same biases. It also helps make the biases of individuals more obvious because if you repeatedly misinterpret information in the same way in multiple experiments or over a period, the other people working on the same question will notice. If you don't correct your bias when others point it out to you, you'll lose your credibility. Other people might then stop believing what you have to say.
Why Is the Scientific Method Important?
When you use the scientific method, your goal is to do research in a fair, unbiased, and repeatable way. The scientific method helps meet these goals because:
It's a systematic approach to problem-solving. It can help you figure out where you're going wrong in your thinking and research if you're not getting helpful answers to your questions. Helpful answers solve problems and keep you moving forward. So, a systematic approach helps you improve your problem-solving abilities if you get stuck.
It can help you solve your problems. The scientific method helps you isolate problems by focusing on what's important. In addition, it can help you make your solutions better every time you go through the process.
It helps you eliminate (or become aware of) your personal biases. It can help you limit the influence of your own personal, preconceived notions . A big part of the process is considering what other people already know and think about your question. It also involves sharing what you've learned and letting other people ask about your methods and conclusions. At the end of the process, even if you still think your answer is best, you have considered what other people know and think about the question.
The scientific method is a systematic way of conducting experiments or studies so that you can explore the world around you and answer questions using reason and evidence. It's a step-by-step problem-solving process that involves: (1) observation, (2) asking questions, (3) forming hypotheses and making predictions, (4) testing your hypotheses through experiments or more observations, (5) using what you learned through experiment or observation to guide further investigation, and (6) sharing your results.
Top doctors in ,
Find more top doctors on, related links.
- Health A-Z News
- Health A-Z Reference
- Health A-Z Slideshows
- Health A-Z Quizzes
- Health A-Z Videos
- WebMDRx Savings Card
- Coronavirus (COVID-19)
- Hepatitis C
- Diabetes Warning Signs
- Rheumatoid Arthritis
- Morning-After Pill
- Breast Cancer Screening
- Psoriatic Arthritis Symptoms
- Heart Failure
- Multiple Myeloma
- Types of Crohn's Disease
- COVID-19 Tracker
- Biochemistry
- Anatomy & Physiology
- Microbiology
- Neuroscience
- Animal Kingdom
- NGSS High School
- Latest News
- Editors’ Picks
- Weekly Digest
- Quotes about Biology
![scientific method hypothesis is Biology Dictionary](https://biologydictionary.net/wp-content/uploads/2019/05/Biology-Dictionary-Logo-272.png)
Scientific Method
Reviewed by: BD Editors
The scientific method is a series of processes that people can use to gather knowledge about the world around them, improve that knowledge, and attempt to explain why and/or how things occur. This method involves making observations, forming questions, making hypotheses, doing an experiment, analyzing the data, and forming a conclusion. Every scientific experiment performed is an example of the scientific method in action, but it is also used by non-scientists in everyday situations.
Scientific Method Overview
The scientific method is a process of trying to get as close as possible to the objective truth . However, part of the process is to constantly refine your conclusions, ask new questions, and continue the search for the rules of the universe. Through the scientific method, scientists are trying to uncover how the world works and discover the laws that make it function in that way. You can use the scientific method to find answers for almost any question, though the scientific method can yield conflicting evidence based on the method of experimentation. In other words, the scientific method is a very useful way to figure things out – though it must be used with caution and care!
![scientific method hypothesis is The scientific method includes making a hypothesis, identifying variables, conducting an experiment, collecting data, and drawing conclusions.](https://biologydictionary.net/wp-content/uploads/2017/03/The-Scientific-Method.jpg)
Scientific Method Steps
The exact steps of the scientific method vary from source to source , but the general procedure is the same: acquiring knowledge through observation and testing.
Making an Observation
The first step of the scientific method is to make an observation about the world around you. Before hypotheses can be made or experiments can be done, one must first notice and think about some sort of phenomena occurring. The scientific method is used when one does not know why or how something is occurring and wants to uncover the answer. But, before you can form a question you must notice something puzzling in the first place.
Asking a Question
Next, one must ask a question based on their observations. Here are some examples of good questions:
- Why is this thing occurring?
- How is this thing occurring?
- Why or how does it happen this way?
Sometimes this step is listed first in the scientific method, with making an observation (and researching the phenomena in question) listed as second. In reality, both making observations and asking questions tend to happen around the same time.
One can see a confusing occurrence and immediately think, “why is it occurring?” When observations are being made and questions are being formed, it is important to do research to see if others have already answered the question or uncovered information that may help you shape your question. For example, if you find an answer to why something is occurring, you may want to go a step further and figure out how it occurs.
Forming a Hypothesis
A hypothesis is an educated guess to explain the phenomena occurring based on prior observations. It answers the question posed in the previous step. Hypotheses can be specific or more general depending on the question being asked, but all hypotheses must be testable by gathering evidence that can be measured. If a hypothesis is not testable, then it is impossible to perform an experiment to determine whether the hypothesis is supported by evidence.
Performing an Experiment
After forming a hypothesis, an experiment must be set up and performed to test the hypothesis. An experiment must have an independent variable (something that is manipulated by the person doing the experiment), and a dependent variable (the thing being measured which may be affected by the independent variable). All other variables must be controlled so that they do not affect the outcome. During an experiment, data is collected. Data is a set of values; it may be quantitative (e.g. measured in numbers) or qualitative (a description or generalization of the results).
![scientific method hypothesis is Two scientists conducting an experiment on farmland soils gather samples to analyze.](https://biologydictionary.net/wp-content/uploads/2017/03/Scientists-gather-data-for-an-experiment.jpg)
For example, if you were to test the effect of sunlight on plant growth, the amount of light would be the independent variable (the thing you manipulate) and the height of the plants would be the dependent variable (the thing affected by the independent variable). Other factors such as air temperature, amount of water in the soil, and species of plant would have to be kept the same between all of the plants used in the experiment so that you could truly collect data on whether sunlight affects plant growth. The data that you would collect would be quantitative – since you would measure the height of the plant in numbers.
Analyzing Data
After performing an experiment and collecting data, one must analyze the data. Research experiments are usually analyzed with statistical software in order to determine relationships among the data. In the case of a simpler experiment, one could simply look at the data and see how they correlate with the change in the independent variable.
Forming a Conclusion
The last step of the scientific method is to form a conclusion. If the data support the hypothesis, then the hypothesis may be the explanation for the phenomena. However, multiple trials must be done to confirm the results, and it is also important to make sure that the sample size—the number of observations made—is big enough so that the data is not skewed by just a few observations.
If the data do not support the hypothesis, then more observations must be made, a new hypothesis is formed, and the scientific method is used all over again. When a conclusion is drawn, the research can be presented to others to inform them of the findings and receive input about the validity of the conclusion drawn from the research.
![scientific method hypothesis is The scientific method is seen as a circular diagram that feeds back into itself - due to the nature of conclusions inspire new hypotheses.](https://biologydictionary.net/wp-content/uploads/2017/03/The-scientific-method-is-an-ongoing-process-that-repeats-itself.jpg)
Scientific Method Examples
There are very many examples of the use of the scientific method throughout history because it is the basis for all scientific experiments. Scientists have been conducting experiments using the scientific method for hundreds of years.
One such example is Francesco Redi’s experiment on spontaneous generation. In the 17 th Century, when Redi lived, people commonly believed that living things could spontaneously arise from organic material. For example, people believed that maggots were created from meat that was left out to sit. Redi had an alternate hypothesis: that maggots were actually part of the fly life cycle!
![scientific method hypothesis is In the Redi experiment, Francesco Redi found that food only grew maggots when flies could access the food - proving that maggots were part of the fly life cycle.](https://biologydictionary.net/wp-content/uploads/2017/03/The-Francesco-Redi-Experiment.jpg)
He conducted an experiment by leaving four jars of meat out: some uncovered, some covered with muslin, and some sealed completely. Flies got into the uncovered jars and maggots appeared a short time later. The jars that were covered had maggots on the outer surface of the muslin, but not inside the jars. Sealed jars had absolutely no maggots whatsoever.
Redi was able to conclude that maggots did not spontaneously arise in meat. He further confirmed the results by collecting captured maggots and growing them into adult flies. This may seem like common sense today, but back then, people did not know as much about the world, and it is through experiments like these that people uncovered what is now common knowledge.
Scientists use the scientific method in their research, but it is also used by people who aren’t scientists in everyday life. Even if you were not consciously aware of it, you have used the scientific method many times when solving problems around you.
![scientific method hypothesis is Conclusions typically lead to new hypotheses because new information always creates new questions.](https://biologydictionary.net/wp-content/uploads/2017/03/Conclusions-from-the-scientific-method-can-lead-to-new-hypotheses.jpg)
For example, say you are at home and a lightbulb goes out. Noticing that the lightbulb is out is an observation. You would then naturally question, “Why is the lightbulb out?” and come up with possible guesses, or hypotheses. For example, you may hypothesize that the bulb has burned out. Then you would perform a very small experiment in order to test your hypothesis; namely, you would replace the bulb and analyze the data (“Did the light come back on?”).
If the light turned back on, you would conclude that the lightbulb had, in fact, burned out. But if the light still did not work, you would come up with other hypotheses (“The socket doesn’t work”, “Part of the lamp is broken,” “The fuse went out”, etc.) and test those.
1. Which step of the scientific method comes immediately after making observations and asking a question?
2. A scientist is performing an experiment to determine if the amount of light that rodents are exposed to affects their sleep cycle. She places some rodents in a room with 12 hours of light and 12 hours of darkness, some in a room with 24-hour light, and some in 24-hour darkness. What is the independent variable in this experiment?
3. What is the last step of the scientific method?
Enter your email to receive results:
Cite This Article
Subscribe to our newsletter, privacy policy, terms of service, scholarship, latest posts, white blood cell, t cell immunity, satellite cells, embryonic stem cells, popular topics, hydrochloric acid, homeostasis, natural selection, cellular respiration, horticulture, mitochondria, acetic acid.
![](http://blog10.website/777/templates/cheerup2/res/banner1.gif)
IMAGES
COMMENTS
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.
scientific method, mathematical and experimental technique employed in the sciences. More specifically, it is the technique used in the construction and testing of a scientific hypothesis. The process of observing, asking questions, and seeking answers through tests and experiments is not unique to any one field of science.
The scientific method. At the core of physics and other sciences lies a problem-solving approach called the scientific method. The scientific method has five basic steps, plus one feedback step: Make an observation. Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis.
The scientific method is an empirical method for acquiring knowledge that has characterized the development of science since at least the 17th century. The scientific method involves careful observation coupled with rigorous scepticism, because cognitive assumptions can distort the interpretation of the observation.Scientific inquiry includes creating a hypothesis through inductive reasoning ...
A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject. In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated ...
Regina Bailey. Updated on August 16, 2024. The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses, and conduct scientific experiments . A scientific inquiry starts with an observation.
Step 2: Hypothesis. Once the problem or question is well defined, the scientist proposes a possible answer, a hypothesis, before conducting an experiment or fieldwork. This hypothesis must be specific, falsifiable, and should be based on other scientific work.
The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA.
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
Learn about the scientific method, including explanations of the six steps in the process, the variables involved, and why each step is important. ... Hypothesis Propose a hypothesis. This is a sort of educated guess about what you expect your research to reveal. A hypothesis is a statement used to predict the outcome of an experiment.
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.
The six steps of the scientific method include: 1) asking a question about something you observe, 2) doing background research to learn what is already known about the topic, 3) constructing a hypothesis, 4) experimenting to test the hypothesis, 5) analyzing the data from the experiment and drawing conclusions, and 6) communicating the results ...
The scientific method is a process that includes several steps: First, an observation or question arises about a phenomenon. Then a hypothesis is formulated to explain the phenomenon, which is used to make predictions about other related occurrences or to predict the results of new observations quantitatively. Finally, these predictions are put to the test through experiments or further ...
Meaning. Biology. The study of living things. Observation. Noticing and describing events in an orderly way. Hypothesis. A scientific explanation that can be tested through experimentation or observation. Controlled experiment. An experiment in which only one variable is changed.
The scientific method is a system scientists and other people use to ask and answer questions about the natural world. In a nutshell, the scientific method works by making observations, asking a question or identifying a problem, and then designing and analyzing an experiment to test a prediction of what you expect will happen.
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 ...
Science is a systematic and logical approach to discovering how things in the universe work. Scientists use the scientific method to make observations, form hypotheses and gather evidence in an ...
When conducting research, the scientific method steps to follow are: Observe what you want to investigate. Ask a research question and make predictions. Test the hypothesis and collect data. Examine the results and draw conclusions. Report and share the results. This process not only allows scientists to investigate and understand different ...
The scientific method, also known as the hypothetico-deductive method, is a series of steps that can help you accurately describe the things you observe or improve your understanding of them ...
The scientific method is a series of processes that people can use to gather knowledge about the world around them, improve that knowledge, and attempt to explain why and/or how things occur. This method involves making observations, forming questions, making hypotheses, doing an experiment, analyzing the data, and forming a conclusion.
Exactly, like others said if you do more experiments, then you will be sure to find out which info is credible. When I did experiments, my teacher asked us to write a summary about the results we got, so I tested out our hypothesis 3 times to find the average out of all of them, and I got pretty good info out of my experiment.
Theory vs. Hypothesis: Basics of the Scientific Method. Written by MasterClass. Last updated: Jun 7, 2021 • 2 min read. Though you may hear the terms "theory" and "hypothesis" used interchangeably, these two scientific terms have drastically different meanings in the world of science.