Definition of the word scientific method

The scientific method is an empirical method for acquiring knowledge that has characterized the development of science since at least the 17th century (with notable practitioners in previous centuries; see the article history of scientific method for additional detail.) It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; the testability of hypotheses, experimental and the measurement-based statistical testing of deductions drawn from the hypotheses; and refinement (or elimination) of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises.^[1][2][3]

Although procedures vary from one field of inquiry to another, the underlying process is frequently the same from one field to another. The process in the scientific method involves making conjectures (hypothetical explanations), deriving predictions from the hypotheses as logical consequences, and then carrying out experiments or empirical observations based on those predictions.^[a][4] A hypothesis is a conjecture, based on knowledge obtained while seeking answers to the question. The hypothesis might be very specific, or it might be broad. Scientists then test hypotheses by conducting experiments or studies. A scientific hypothesis must be falsifiable, implying that it is possible to identify a possible outcome of an experiment or observation that conflicts with predictions deduced from the hypothesis; otherwise, the hypothesis cannot be meaningfully tested.^[5]

The purpose of an experiment is to determine whether observations^[A][a][b] agree with or conflict with the expectations deduced from a hypothesis.^{[6]: Book I, [6.54] pp.372, 408 [b]} Experiments can take place anywhere from a garage to a remote mountaintop to CERN’s Large Hadron Collider. There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, it represents rather a set of general principles.^[7] Not all steps take place in every scientific inquiry (nor to the same degree), and they are not always in the same order.^[8][9]

History

Important debates in the history of science concern skepticism that anything can be known for sure (such as views of Francisco Sanches), rationalism (especially as advocated by René Descartes), inductivism, empiricism (as argued for by Francis Bacon, then rising to particular prominence with Isaac Newton and his followers), and hypothetico-deductivism, which came to the fore in the early 19th century.

The term «scientific method» emerged in the 19th century, when a significant institutional development of science was taking place and terminologies establishing clear boundaries between science and non-science, such as «scientist» and «pseudoscience», appeared.^[16] Throughout the 1830s and 1850s, at which time Baconianism was popular, naturalists like William Whewell, John Herschel, John Stuart Mill engaged in debates over «induction» and «facts» and were focused on how to generate knowledge.^[16] In the late 19th and early 20th centuries, a debate over realism vs. antirealism was conducted as powerful scientific theories extended beyond the realm of the observable.^[17]

Problem-solving via scientific method

See Notes section § Problem-solving via scientific method

The term «scientific method» came into popular use in the twentieth century; Dewey’s 1910 book, How We Think, inspired popular guidelines,^[18] popping up in dictionaries and science textbooks, although there was little consensus over its meaning.^[16] Although there was growth through the middle of the twentieth century, by the 1960s and 1970s numerous influential philosophers of science such as Thomas Kuhn and Paul Feyerabend had questioned the universality of the «scientific method» and in doing so largely replaced the notion of science as a homogeneous and universal method with that of it being a heterogeneous and local practice.^[16] In particular, Paul Feyerabend, in the 1975 first edition of his book Against Method, argued against there being any universal rules of science;^[17] Popper 1963,^[19] Gauch 2003,^[7] and Tow 2010^[20] disagree with Feyerabend’s claim; problem solvers, and researchers are to be prudent with their resources during their inquiry.^[B][c]

Later stances include physicist Lee Smolin’s 2013 essay «There Is No Scientific Method»,^[26] in which he espouses two ethical principles,^[e] and historian of science Daniel Thurs’s chapter in the 2015 book Newton’s Apple and Other Myths about Science, which concluded that the scientific method is a myth or, at best, an idealization.^[27] As myths are beliefs,^[28] they are subject to the narrative fallacy as Taleb points out.^[29] Philosophers Robert Nola and Howard Sankey, in their 2007 book Theories of Scientific Method, said that debates over scientific method continue, and argued that Feyerabend, despite the title of Against Method, accepted certain rules of method and attempted to justify those rules with a meta methodology.^[30]
Staddon (2017) argues it is a mistake to try following rules in the absence of an algorithmic scientific method; in that case, «science is best understood through examples».^[f] But algorithmic methods, such as disproof of existing theory by experiment have been used since Alhacen (1027) Book of Optics,^[b] and Galileo (1638) Two New Sciences,^[32] and The Assayer^[33] still stand as scientific method. They contradict Feyerabend’s stance.
^[C][D]

The ubiquitous element in the scientific method is empiricism. This is in opposition to stringent forms of rationalism: the scientific method embodies the position that reason alone cannot solve a particular scientific problem. A strong formulation of the scientific method is not always aligned with a form of empiricism in which the empirical data is put forward in the form of experience or other abstracted forms of knowledge; in current scientific practice, however, the use of scientific modelling and reliance on abstract typologies and theories is normally accepted. The scientific method counters claims that revelation, political or religious dogma, appeals to tradition, commonly held beliefs, common sense, or currently held theories pose the only possible means of demonstrating truth.^[37][21][20]

Different early expressions of empiricism and the scientific method can be found throughout history, for instance with the ancient Stoics, Epicurus,^[38] Alhazen,^[E] Avicenna, Roger Bacon, and William of Ockham. From the 16th century onwards, experiments were advocated by Francis Bacon, and performed by Giambattista della Porta,^[39] Johannes Kepler,^[40][i] and Galileo Galilei.^[j] There was particular development aided by theoretical works by Francisco Sanches,^[41] John Locke, George Berkeley, and David Hume.

A sea voyage from America to Europe afforded C. S. Peirce the distance to clarify his ideas,^[F] gradually resulting in the hypothetico-deductive model.^[42] Formulated in the 20th century, the model has undergone significant revision since first proposed (for a more formal discussion, see § Elements of the scientific method).

Overview

The DNA example below is a synopsis of this method.

The scientific method is the process by which science is carried out.^[43] As in other areas of inquiry, science (through the scientific method) can build on previous knowledge and develop a more sophisticated understanding of its topics of study over time.^{[k][45][46][47][48][49][50]} This model can be seen to underlie the scientific revolution.^[51]

Process

The overall process involves making conjectures (hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original conjecture was correct.^[4] There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, these actions are better considered as general principles.^[8] Not all steps take place in every scientific inquiry (nor to the same degree), and they are not always done in the same order. As noted by scientist and philosopher William Whewell (1794–1866), «invention, sagacity, [and] genius»^[9] are required at every step.

Formulation of a question

The question can refer to the explanation of a specific observation,^[A] as in «Why is the sky blue?» but can also be open-ended, as in «How can I design a drug to cure this particular disease?» This stage frequently involves finding and evaluating evidence from previous experiments, personal scientific observations or assertions, as well as the work of other scientists. If the answer is already known, a different question that builds on the evidence can be posed. When applying the scientific method to research, determining a good question can be very difficult and it will affect the outcome of the investigation.^[52]

Hypothesis

A hypothesis is a conjecture, based on knowledge obtained while formulating the question, that may explain any given behavior. The hypothesis might be very specific; for example, Einstein’s equivalence principle or Francis Crick’s «DNA makes RNA makes protein»,^[l] or it might be broad; for example, «unknown species of life dwell in the unexplored depths of the oceans». See § Hypothesis development

A statistical hypothesis is a conjecture about a given statistical population. For example, the population might be people with a particular disease. One conjecture might be that a new drug will cure the disease in some of the people in that population, as in a clinical trial of the drug.^[53] A null hypothesis would conjecture that the statistical hypothesis is false; for example, that the new drug does nothing, and that any cure in the population would be caused by chance (a random variable).

An alternative to the null hypothesis, to be falsifiable, must say that a treatment program with the drug does better than chance. To test the statement a treatment program with the drug does better than chance, an experiment is designed in which a portion of the population (the control group), is to be left untreated, while another, separate portion of the population is to be treated.^[54] t-Tests could then specify how large the treated groups, and how large the control groups are to be, in order to infer whether some course of treatment of the population has resulted in a cure of some of them, in each of the groups.^[m] The groups are examined, in turn by the researchers, in a protocol.^[n]

Strong inference could alternatively propose multiple alternative hypotheses embodied in randomized controlled trials, treatments A, B, C, … , (say in a blinded experiment with varying dosages, or with lifestyle changes, and so forth) so as not to introduce confirmation bias in favor of a specific course of treatment.^[56] Ethical considerations could be used, to minimize the numbers in the untreated groups, e.g., use almost every treatment in every group, but excluding A, B, C, …, respectively as controls.^[o][p]

Prediction

The prediction step deduces the logical consequences of the hypothesis before the outcome is known. These predictions are expectations for the results of testing. If the result is already known, it is evidence that is ready to be considered in acceptance or rejection of the hypothesis.
The evidence is also stronger if the actual result of the predictive test is not already known, as tampering with the test can be ruled out, as can hindsight bias (see postdiction). Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. (These statements about the relative strength of evidence can be mathematically derived using Bayes’ Theorem).^[q]

The consequence, therefore, is to be stated at the same time or briefly after the statement of the hypothesis, but before the experimental result is known.

Likewise, the test protocol is to be stated before execution of the test. These requirements become precautions against tampering, and aid the reproducibility of the experiment.

Testing

Suitable tests^[23][22] of a hypothesis compare the expected values from the tests of that hypothesis with the actual results of those tests. Scientists (and other people) can then secure, or discard, their hypotheses by conducting suitable experiments.

Analysis

An analysis determines, from the results of the experiment, the next actions to take. The expected values from the test of the alternative hypothesis are compared to the expected values resulting from the null hypothesis (that is, a prediction of no difference in the status quo). The difference between expected versus actual indicates which hypothesis better explains the resulting data from the experiment. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test whether the null hypothesis is true, may be required.

Evidence from other scientists, and from experience are available for incorporation at any stage in the process. Depending on the complexity of the experiment, iteration of the process may be required to gather sufficient evidence to answer the question with confidence, or to build up other answers to highly specific questions, to answer a single broader question.

When the evidence has falsified the alternative hypothesis, a new hypothesis is required; if the evidence does not conclusively justify discarding the alternative hypothesis, other predictions from the alternative hypothesis might be considered. Pragmatic considerations, such as the resources available to continue inquiry, might guide the investigation’s further course.^[B] When evidence for a hypothesis strongly supports that hypothesis, further questioning can follow, for insight into the broader inquiry under investigation.

DNA example

The basic elements of the scientific method are illustrated by the following example (which occurred from 1944 to 1953) from the discovery of the structure of DNA:

• Question: Previous investigation of DNA had determined its chemical composition (the four nucleotides), the structure of each individual nucleotide, and other properties. DNA had been identified as the carrier of genetic information by the Avery–MacLeod–McCarty experiment in 1944,^[57] but the mechanism of how genetic information was stored in DNA was unclear.^[58]
• Hypothesis: Linus Pauling, Francis Crick and James D. Watson hypothesized that DNA had a helical structure.^[59]
• Prediction: If DNA had a helical structure, its X-ray diffraction pattern would be X-shaped.^[60][61] This prediction was determined using the mathematics of the helix transform, which had been derived by Cochran, Crick, and Vand^[62] (and independently by Stokes). This prediction was a mathematical construct, completely independent from the biological problem at hand.
• Experiment: Rosalind Franklin used pure DNA to perform X-ray diffraction to produce photo 51. The results showed an X-shape.
• Analysis: When Watson saw the detailed diffraction pattern, he immediately recognized it as a helix.^[24][63][c] He and Crick then produced their model, using this information along with the previously known information about DNA’s composition, especially Chargaff’s rules of base pairing.^[25]

The discovery became the starting point for many further studies involving the genetic material, such as the field of molecular genetics, and it was awarded the Nobel Prize in 1962. Each step of the example is examined in more detail later in the article.

Other components

The scientific method also includes other components required even when all the iterations of the steps above have been completed:^[32]

Replication

If an experiment cannot be repeated to produce the same results, this implies that the original results might have been in error. As a result, it is common for a single experiment to be performed multiple times, especially when there are uncontrolled variables or other indications of experimental error. For significant or surprising results, other scientists may also attempt to replicate the results for themselves, especially if those results would be important to their own work.^[64]
Replication has become a contentious issue in social and biomedical science where treatments are administered to groups of individuals. Typically an experimental group gets the treatment, such as a drug, and the control group gets a placebo. John Ioannidis in 2005 pointed out that the method being used has led to many findings that cannot be replicated.^[65]

External review

The process of peer review involves evaluation of the experiment by experts, who typically give their opinions anonymously. Some journals request that the experimenter provide lists of possible peer reviewers, especially if the field is highly specialized. Peer review does not certify the correctness of the results, only that, in the opinion of the reviewer, the experiments themselves were sound (based on the description supplied by the experimenter). If the work passes peer review, which occasionally may require new experiments requested by the reviewers, it will be published in a peer-reviewed scientific journal. The specific journal that publishes the results indicates the perceived quality of the work.^[r]

Data recording and sharing

Scientists typically are careful in recording their data, a requirement promoted by Ludwik Fleck (1896–1961) and others.^[66] Though not typically required, they might be requested to supply this data to other scientists who wish to replicate their original results (or parts of their original results), extending to the sharing of any experimental samples that may be difficult to obtain.^[67] See §Communication and community.

Instrumentation

See scientific community, big science.

Institutional researchers might acquire an instrument to institutionalize their tests. These instruments would use observations of the real world, which might agree with, or perhaps conflict with, their predictions deduced from their hypothesis. These institutions thereby reduce the research function to a cost/benefit,^[68] which is expressed as money, and the time and attention of the researchers to be expended,^[68] in exchange for a report to their constituents.^[69]

Current large instruments, such as CERN’s Large Hadron Collider (LHC),^[70] or LIGO,^[71] or the National Ignition Facility (NIF),^[72] or the International Space Station (ISS),^[73] or the James Webb Space Telescope (JWST),^[74][75] entail expected costs of billions of dollars, and timeframes extending over decades. These kinds of institutions affect public policy, on a national or even international basis, and the researchers would require shared access to such machines and their adjunct infrastructure.^[s][76] See Perceptual control theory, §Open-loop and closed-loop feedback

Elements of the scientific method

There are different ways of outlining the basic method used for scientific inquiry. The scientific community and philosophers of science generally agree on the following classification of method components. These methodological elements and organization of procedures tend to be more characteristic of experimental sciences than social sciences. Nonetheless, the cycle of formulating hypotheses, testing and analyzing the results, and formulating new hypotheses, will resemble the cycle described below.

The scientific method is an iterative, cyclical process through which information is continually revised.^[77][78] It is generally recognized to develop advances in knowledge through the following elements, in varying combinations or contributions:^[46][49]

• Characterizations (observations, definitions, and measurements of the subject of inquiry)
• Hypotheses (theoretical, hypothetical explanations of observations and measurements of the subject)
• Predictions (inductive and deductive reasoning from the hypothesis or theory)
• Experiments (tests of all of the above)

Each element of the scientific method is subject to peer review for possible mistakes. These activities do not describe all that scientists do but apply mostly to experimental sciences (e.g., physics, chemistry, biology, and psychology). The elements above are often taught in the educational system as «the scientific method».^[A]

The scientific method is not a single recipe: it requires intelligence, imagination, and creativity.^[79] In this sense, it is not a mindless set of standards and procedures to follow, but is rather an ongoing cycle, constantly developing more useful, accurate, and comprehensive models and methods. For example, when Einstein developed the Special and General Theories of Relativity, he did not in any way refute or discount Newton’s Principia. On the contrary, if the astronomically massive, the feather-light, and the extremely fast are removed from Einstein’s theories – all phenomena Newton could not have observed – Newton’s equations are what remain. Einstein’s theories are expansions and refinements of Newton’s theories and, thus, increase confidence in Newton’s work.

An iterative,^[78] pragmatic^[37] scheme of the four points above is sometimes offered as a guideline for proceeding:^[80]

1. Define a question
2. Gather information and resources (observe)
3. Form an explanatory hypothesis
4. Test the hypothesis by performing an experiment and collecting data in a reproducible manner
5. Analyze the data
6. Interpret the data and draw conclusions that serve as a starting point for a new hypothesis
7. Publish results
8. Retest (frequently done by other scientists)

The iterative cycle inherent in this step-by-step method goes from point 3 to 6 back to 3 again.

While this schema outlines a typical hypothesis/testing method,^[81] many philosophers, historians, and sociologists of science, including Paul Feyerabend,^[t] claim that such descriptions of scientific method have little relation to the ways that science is actually practiced.

Characterizations

The scientific method depends upon increasingly sophisticated characterizations of the subjects of investigation. (The subjects can also be called unsolved problems or the unknowns.)^[A] For example, Benjamin Franklin conjectured, correctly, that St. Elmo’s fire was electrical in nature, but it has taken a long series of experiments and theoretical changes to establish this. While seeking the pertinent properties of the subjects, careful thought may also entail some definitions and observations; the observations often demand careful measurements and/or counting.

The systematic, careful collection of measurements or counts of relevant quantities is often the critical difference between pseudo-sciences, such as alchemy, and science, such as chemistry or biology. Scientific measurements are usually tabulated, graphed, or mapped, and statistical manipulations, such as correlation and regression, performed on them. The measurements might be made in a controlled setting, such as a laboratory, or made on more or less inaccessible or unmanipulatable objects such as stars or human populations. The measurements often require specialized scientific instruments such as thermometers, spectroscopes, particle accelerators, or voltmeters, and the progress of a scientific field is usually intimately tied to their invention and improvement.

I am not accustomed to saying anything with certainty after only one or two observations.

Uncertainty

Measurements in scientific work are also usually accompanied by estimates of their uncertainty.^[68] The uncertainty is often estimated by making repeated measurements of the desired quantity. Uncertainties may also be calculated by consideration of the uncertainties of the individual underlying quantities used. Counts of things, such as the number of people in a nation at a particular time, may also have an uncertainty due to data collection limitations. Or counts may represent a sample of desired quantities, with an uncertainty that depends upon the sampling method used and the number of samples taken.

Definition

Measurements demand the use of operational definitions of relevant quantities. That is, a scientific quantity is described or defined by how it is measured, as opposed to some more vague, inexact, or «idealized» definition. For example, electric current, measured in amperes, may be operationally defined in terms of the mass of silver deposited in a certain time on an electrode in an electrochemical device that is described in some detail. The operational definition of a thing often relies on comparisons with standards: the operational definition of «mass» ultimately relies on the use of an artifact, such as a particular kilogram of platinum-iridium kept in a laboratory in France.

The scientific definition of a term sometimes differs substantially from its natural language usage. For example, mass and weight overlap in meaning in common discourse, but have distinct meanings in mechanics. Scientific quantities are often characterized by their units of measure which can later be described in terms of conventional physical units when communicating the work.

New theories are sometimes developed after realizing certain terms have not previously been sufficiently clearly defined. For example, Albert Einstein’s first paper on relativity begins by defining simultaneity and the means for determining length. These ideas were skipped over by Isaac Newton with, «I do not define time, space, place and motion, as being well known to all.» Einstein’s paper then demonstrates that they (viz., absolute time and length independent of motion) were approximations. Francis Crick cautions us that when characterizing a subject, however, it can be premature to define something when it remains ill-understood.^[84] In Crick’s study of consciousness, he actually found it easier to study awareness in the visual system, rather than to study free will, for example. His cautionary example was the gene; the gene was much more poorly understood before Watson and Crick’s pioneering discovery of the structure of DNA; it would have been counterproductive to spend much time on the definition of the gene, before them.

DNA-characterizations

The history of the discovery of the structure of DNA is a classic example of the elements of the scientific method: in 1950 it was known that genetic inheritance had a mathematical description, starting with the studies of Gregor Mendel, and that DNA contained genetic information (Oswald Avery’s transforming principle).^[57] But the mechanism of storing genetic information (i.e., genes) in DNA was unclear. Researchers in Bragg’s laboratory at Cambridge University made X-ray diffraction pictures of various molecules, starting with crystals of salt, and proceeding to more complicated substances. Using clues painstakingly assembled over decades, beginning with its chemical composition, it was determined that it should be possible to characterize the physical structure of DNA, and the X-ray images would be the vehicle.^[85] ..2. DNA-hypotheses

Another example: precession of Mercury

The characterization element can require extended and extensive study, even centuries. It took thousands of years of measurements, from the Chaldean, Indian, Persian, Greek, Arabic, and European astronomers, to fully record the motion of planet Earth. Newton was able to include those measurements into the consequences of his laws of motion. But the perihelion of the planet Mercury’s orbit exhibits a precession that cannot be fully explained by Newton’s laws of motion (see diagram to the right), as Leverrier pointed out in 1859. The observed difference for Mercury’s precession between Newtonian theory and observation was one of the things that occurred to Albert Einstein as a possible early test of his theory of General relativity. His relativistic calculations matched observation much more closely than did Newtonian theory. The difference is approximately 43 arc-seconds per century.

Hypothesis development

A hypothesis is a suggested explanation of a phenomenon, or alternately a reasoned proposal suggesting a possible correlation between or among a set of phenomena.

Normally hypotheses have the form of a mathematical model. Sometimes, but not always, they can also be formulated as existential statements, stating that some particular instance of the phenomenon being studied has some characteristic and causal explanations, which have the general form of universal statements, stating that every instance of the phenomenon has a particular characteristic.

Scientists are free to use whatever resources they have – their own creativity, ideas from other fields, inductive reasoning, Bayesian inference, and so on – to imagine possible explanations for a phenomenon under study. Albert Einstein once observed that «there is no logical bridge between phenomena and their theoretical principles.»^[87][u] Charles Sanders Peirce, borrowing a page from Aristotle (Prior Analytics, 2.25)^[89] described the incipient stages of inquiry, instigated by the «irritation of doubt» to venture a plausible guess, as abductive reasoning.^{[34]: II, p.290} The history of science is filled with stories of scientists claiming a «flash of inspiration», or a hunch, which then motivated them to look for evidence to support or refute their idea. Michael Polanyi made such creativity the centerpiece of his discussion of methodology.

William Glen observes that^[90]

the success of a hypothesis, or its service to science, lies not simply in its perceived «truth», or power to displace, subsume or reduce a predecessor idea, but perhaps more in its ability to stimulate the research that will illuminate … bald suppositions and areas of vagueness.

— William Glen, The Mass-Extinction Debates

In general scientists tend to look for theories that are «elegant» or «beautiful». Scientists often use these terms to refer to a theory that is following the known facts but is nevertheless relatively simple and easy to handle. Occam’s Razor serves as a rule of thumb for choosing the most desirable amongst a group of equally explanatory hypotheses.

To minimize the confirmation bias which results from entertaining a single hypothesis, strong inference emphasizes the need for entertaining multiple alternative hypotheses.^[56]

DNA-hypotheses

Linus Pauling proposed that DNA might be a triple helix.^[91] This hypothesis was also considered by Francis Crick and James D. Watson but discarded. When Watson and Crick learned of Pauling’s hypothesis, they understood from existing data that Pauling was wrong.^[92] and that Pauling would soon admit his difficulties with that structure. So, the race was on to figure out the correct structure (except that Pauling did not realize at the time that he was in a race) ..3. DNA-predictions

Predictions from the hypothesis

Any useful hypothesis will enable predictions, by reasoning including deductive reasoning. It might predict the outcome of an experiment in a laboratory setting or the observation of a phenomenon in nature. The prediction can also be statistical and deal only with probabilities.

It is essential that the outcome of testing such a prediction be currently unknown. Only in this case does a successful outcome increase the probability that the hypothesis is true. If the outcome is already known, it is called a consequence and should have already been considered while formulating the hypothesis.

If the predictions are not accessible by observation or experience, the hypothesis is not yet testable and so will remain to that extent unscientific in a strict sense. A new technology or theory might make the necessary experiments feasible. For example, while a hypothesis on the existence of other intelligent species may be convincing with scientifically based speculation, no known experiment can test this hypothesis. Therefore, science itself can have little to say about the possibility. In the future, a new technique may allow for an experimental test and the speculation would then become part of accepted science.

DNA-predictions

James D. Watson, Francis Crick, and others hypothesized that DNA had a helical structure. This implied that DNA’s X-ray diffraction pattern would be ‘x shaped’.^[61][60] This prediction followed from the work of Cochran, Crick and Vand^[62] (and independently by Stokes). The Cochran-Crick-Vand-Stokes theorem provided a mathematical explanation for the empirical observation that diffraction from helical structures produces x shaped patterns.

In their first paper, Watson and Crick also noted that the double helix structure they proposed provided a simple mechanism for DNA replication, writing, «It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material».^[93] ..4. DNA-experiments

Another example: general relativity

Einstein’s theory of general relativity makes several specific predictions about the observable structure of spacetime, such as that light bends in a gravitational field, and that the amount of bending depends in a precise way on the strength of that gravitational field. Arthur Eddington’s observations made during a 1919 solar eclipse supported General Relativity rather than Newtonian gravitation.^[94]

Experiments

Once predictions are made, they can be sought by experiments. If the test results contradict the predictions, the hypotheses which entailed them are called into question and become less tenable. Sometimes the experiments are conducted incorrectly or are not very well designed when compared to a crucial experiment. If the experimental results confirm the predictions, then the hypotheses are considered more likely to be correct, but might still be wrong and continue to be subject to further testing. The experimental control is a technique for dealing with observational error. This technique uses the contrast between multiple samples, or observations, or populations, under differing conditions, to see what varies or what remains the same. We vary the conditions for the acts of measurement, to help isolate what has changed. Mill’s canons can then help us figure out what the important factor is.^[95] Factor analysis is one technique for discovering the important factor in an effect.

Depending on the predictions, the experiments can have different shapes. It could be a classical experiment in a laboratory setting, a double-blind study or an archaeological excavation. Even taking a plane from New York to Paris is an experiment that tests the aerodynamical hypotheses used for constructing the plane.

Scientists assume an attitude of openness and accountability on the part of those experimenting. Detailed record-keeping is essential, to aid in recording and reporting on the experimental results, and supports the effectiveness and integrity of the procedure. They will also assist in reproducing the experimental results, likely by others. Traces of this approach can be seen in the work of Hipparchus (190–120 BCE), when determining a value for the precession of the Earth, while controlled experiments can be seen in the works of al-Battani (853–929 CE)^[96] and Alhazen (965–1039 CE).^[6][v][w][g]

DNA-experiments

Watson and Crick showed an initial (and incorrect) proposal for the structure of DNA to a team from King’s College London – Rosalind Franklin, Maurice Wilkins, and Raymond Gosling. Franklin immediately spotted the flaws which concerned the water content. Later Watson saw Franklin’s detailed X-ray diffraction images which showed an X-shape^[98] and was able to confirm the structure was helical.^[24][63] This rekindled Watson and Crick’s model building and led to the correct structure. ..1. DNA-characterizations

Evaluation and improvement

The scientific method is iterative. At any stage, it is possible to refine its accuracy and precision, so that some consideration will lead the scientist to repeat an earlier part of the process. Failure to develop an interesting hypothesis may lead a scientist to re-define the subject under consideration. Failure of a hypothesis to produce interesting and testable predictions may lead to reconsideration of the hypothesis or of the definition of the subject. Failure of an experiment to produce interesting results may lead a scientist to reconsider the experimental method, the hypothesis, or the definition of the subject.

By 1027, Alhazen, based on his measurements of the refraction of light, was able to deduce that outer space was less dense than air, that is: «the body of the heavens is rarer than the body of air».^[36] In 1079 Ibn Mu’adh’s Treatise On Twilight was able to infer that Earth’s atmosphere was 50 miles thick, based on atmospheric refraction of the sun’s rays.^[x]

Other scientists may start their own research and enter the process at any stage. They might adopt the characterization and formulate their own hypothesis, or they might adopt the hypothesis and deduce their own predictions. Often the experiment is not done by the person who made the prediction, and the characterization is based on experiments done by someone else. Published results of experiments can also serve as a hypothesis predicting their own reproducibility.

DNA-iterations

After considerable fruitless experimentation, being discouraged by their superior from continuing, and numerous false starts,^{[100][101][102]} Watson and Crick were able to infer the essential structure of DNA by concrete modeling of the physical shapes of the nucleotides which comprise it.^{[25][103][104]} They were guided by the bond lengths which had been deduced by Linus Pauling and by Rosalind Franklin’s X-ray diffraction images. ..DNA Example

Confirmation

Science is a social enterprise, and scientific work tends to be accepted by the scientific community when it has been confirmed. Crucially, experimental and theoretical results must be reproduced by others within the scientific community. Researchers have given their lives for this vision; Georg Wilhelm Richmann was killed by ball lightning (1753) when attempting to replicate the 1752 kite-flying experiment of Benjamin Franklin.^[105]

To protect against bad science and fraudulent data, government research-granting agencies such as the National Science Foundation, and science journals, including Nature and Science, have a policy that researchers must archive their data and methods so that other researchers can test the data and methods and build on the research that has gone before. Scientific data archiving can be done at several national archives in the U.S. or the World Data Center.

Scientific inquiry

Scientific inquiry generally aims to obtain knowledge in the form of testable explanations^[23][22] that scientists can use to predict the results of future experiments. This allows scientists to gain a better understanding of the topic under study, and later to use that understanding to intervene in its causal mechanisms (such as to cure disease). The better an explanation is at making predictions, the more useful it frequently can be, and the more likely it will continue to explain a body of evidence better than its alternatives. The most successful explanations – those which explain and make accurate predictions in a wide range of circumstances – are often called scientific theories.^[A]

Most experimental results do not produce large changes in human understanding; improvements in theoretical scientific understanding typically result from a gradual process of development over time, sometimes across different domains of science.^[106] Scientific models vary in the extent to which they have been experimentally tested and for how long, and in their acceptance in the scientific community. In general, explanations become accepted over time as evidence accumulates on a given topic, and the explanation in question proves more powerful than its alternatives at explaining the evidence. Often subsequent researchers re-formulate the explanations over time, or combined explanations to produce new explanations.

Tow sees the scientific method in terms of an evolutionary algorithm applied to science and technology.^[20] See Ceteris paribus, and Mutatis mutandis

Properties of scientific inquiry

Scientific knowledge is closely tied to empirical findings and can remain subject to falsification if new experimental observations are incompatible with what is found. That is, no theory can ever be considered final since new problematic evidence might be discovered. If such evidence is found, a new theory may be proposed, or (more commonly) it is found that modifications to the previous theory are sufficient to explain the new evidence. The strength of a theory relates to how long it has persisted without major alteration to its core principles (see invariant explanations).

Theories can also become subsumed by other theories. For example, Newton’s laws explained thousands of years of scientific observations of the planets almost perfectly. However, these laws were then determined to be special cases of a more general theory (relativity), which explained both the (previously unexplained) exceptions to Newton’s laws and predicted and explained other observations such as the deflection of light by gravity. Thus, in certain cases independent, unconnected, scientific observations can be connected, unified by principles of increasing explanatory power.^[107][108]

Since new theories might be more comprehensive than what preceded them, and thus be able to explain more than previous ones, successor theories might be able to meet a higher standard by explaining a larger body of observations than their predecessors.^[107] For example, the theory of evolution explains the diversity of life on Earth, how species adapt to their environments, and many other patterns observed in the natural world;^[109][110] its most recent major modification was unification with genetics to form the modern evolutionary synthesis. In subsequent modifications, it has also subsumed aspects of many other fields such as biochemistry and molecular biology.^[20]

Beliefs and biases

Scientific methodology often directs that hypotheses be tested in controlled conditions wherever possible. This is frequently possible in certain areas, such as in the biological sciences, and more difficult in other areas, such as in astronomy.

The practice of experimental control and reproducibility can have the effect of diminishing the potentially harmful effects of circumstance, and to a degree, personal bias. For example, pre-existing beliefs can alter the interpretation of results, as in confirmation bias; this is a heuristic that leads a person with a particular belief to see things as reinforcing their belief, even if another observer might disagree (in other words, people tend to observe what they expect to observe).^[28]

[T]he action of thought is excited by the irritation of doubt, and ceases when belief is attained.

— C.S. Peirce, How to Make Our Ideas Clear (1877)^[34]

A historical example is the belief that the legs of a galloping horse are splayed at the point when none of the horse’s legs touch the ground, to the point of this image being included in paintings by its supporters. However, the first stop-action pictures of a horse’s gallop by Eadweard Muybridge showed this to be false, and that the legs are instead gathered together.^[111]

Another important human bias that plays a role is a preference for new, surprising statements (see Appeal to novelty), which can result in a search for evidence that the new is true.^[112] Poorly attested beliefs can be believed and acted upon via a less rigorous heuristic.^[113]

Goldhaber and Nieto published in 2010 the observation that if theoretical structures with «many closely neighboring subjects are described by connecting theoretical concepts, then the theoretical structure acquires a robustness which makes it increasingly hard – though certainly never impossible – to overturn».^[108] When a narrative is constructed its elements become easier to believe.^[114][29]

Fleck 1979, p. 27 notes «Words and ideas are originally phonetic and mental equivalences of the experiences coinciding with them. … Such proto-ideas are at first always too broad and insufficiently specialized. … Once a structurally complete and closed system of opinions consisting of many details and relations has been formed, it offers enduring resistance to anything that contradicts it». Sometimes, these relations have their elements assumed a priori, or contain some other logical or methodological flaw in the process that ultimately produced them. Donald M. MacKay has analyzed these elements in terms of limits to the accuracy of measurement and has related them to instrumental elements in a category of measurement.^[y]

Models of scientific inquiry

Classical model

The classical model of scientific inquiry derives from Aristotle,^[115] who distinguished the forms of approximate and exact reasoning, set out the threefold scheme of abductive, deductive, and inductive inference, and also treated the compound forms such as reasoning by analogy.

Hypothetico-deductive model

The hypothetico-deductive model or method is a proposed description of the scientific method. Here, predictions from the hypothesis are central: if you assume the hypothesis to be true, what consequences follow?

If a subsequent empirical investigation does not demonstrate that these consequences or predictions correspond to the observable world, the hypothesis can be concluded to be false.

Pragmatic model

In 1877,^[46] Charles Sanders Peirce (1839–1914) characterized inquiry in general not as the pursuit of truth per se but as the struggle to move from irritating, inhibitory doubts born of surprises, disagreements, and the like, and to reach a secure belief, the belief being that on which one is prepared to act. He framed scientific inquiry as part of a broader spectrum and as spurred, like inquiry generally, by actual doubt, not mere verbal or hyperbolic doubt, which he held to be fruitless.^[z] He outlined four methods of settling opinion, ordered from least to most successful:

1. The method of tenacity (policy of sticking to initial belief) – which brings comforts and decisiveness but leads to trying to ignore contrary information and others’ views as if truth were intrinsically private, not public. It goes against the social impulse and easily falters since one may well notice when another’s opinion is as good as one’s own initial opinion. Its successes can shine but tend to be transitory.^[aa]
2. The method of authority – which overcomes disagreements but sometimes brutally. Its successes can be majestic and long-lived, but it cannot operate thoroughly enough to suppress doubts indefinitely, especially when people learn of other societies’ present and past.
3. The method of the a priori – which promotes conformity less brutally but fosters opinions as something like tastes, arising in conversation and comparisons of perspectives in terms of «what is agreeable to reason.» Thereby it depends on fashion in paradigms and goes in circles over time. It is more intellectual and respectable but, like the first two methods, sustains accidental and capricious beliefs, destining some minds to doubt it.
4. The scientific method – the method wherein inquiry regards itself as fallible and purposely tests itself and criticizes, corrects, and improves itself.

Peirce held that slow, stumbling ratiocination can be dangerously inferior to instinct and traditional sentiment in practical matters, and that the scientific method is best suited to theoretical research,^[118] which in turn should not be trammeled by the other methods and practical ends; reason’s «first rule» is that, in order to learn, one must desire to learn and, as a corollary, must not block the way of inquiry.^[21] The scientific method excels the others by being deliberately designed to arrive – eventually – at the most secure beliefs, upon which the most successful practices can be based. Starting from the idea that people seek not truth per se but instead to subdue irritating, inhibitory doubt, Peirce showed how, through the struggle, some can come to submit to the truth for the sake of belief’s integrity, seek as truth the guidance of potential practice correctly to its given goal, and wed themselves to the scientific method.^[46][49]

For Peirce, rational inquiry implies presuppositions about truth and the real; to reason is to presuppose (and at least to hope), as a principle of the reasoner’s self-regulation, that the real is discoverable and independent of our vagaries of opinion. In that vein, he defined truth as the correspondence of a sign (in particular, a proposition) to its object and, pragmatically, not as the actual consensus of some definite, finite community (such that to inquire would be to poll the experts), but instead as that final opinion which all investigators would reach sooner or later but still inevitably, if they were to push investigation far enough, even when they start from different points.^[34] In tandem he defined the real as a true sign’s object (be that object a possibility or quality, or an actuality or brute fact, or a necessity or norm or law), which is what it is independently of any finite community’s opinion and, pragmatically, depends only on the final opinion destined in a sufficient investigation. That is a destination as far, or near, as the truth itself to you or me or the given finite community. Thus, his theory of inquiry boils down to «Do the science.» Those conceptions of truth and the real involve the idea of a community both without definite limits (and thus potentially self-correcting as far as needed) and capable of definite increase of knowledge.^[119] As inference, «logic is rooted in the social principle» since it depends on a standpoint that is, in a sense, unlimited.^[120]

Paying special attention to the generation of explanations, Peirce outlined the scientific method as coordination of three kinds of inference in a purposeful cycle aimed at settling doubts, as follows (in §III–IV in «A Neglected Argument»^[4] except as otherwise noted):

1. Abduction (or retroduction). Guessing, inference to explanatory hypotheses for selection of those best worth trying. From abduction, Peirce distinguishes induction as inferring, based on tests, the proportion of truth in the hypothesis. Every inquiry, whether into ideas, brute facts, or norms and laws, arises from surprising observations in one or more of those realms (and for example at any stage of an inquiry already underway). All explanatory content of theories comes from abduction, which guesses a new or outside idea to account in a simple, economical way for a surprising or complicative phenomenon. Oftenest, even a well-prepared mind guesses wrong. But the modicum of success of our guesses far exceeds that of sheer luck and seems born of attunement to nature by instincts developed or inherent, especially insofar as best guesses are optimally plausible and simple in the sense, said Peirce, of the «facile and natural», as by Galileo’s natural light of reason and as distinct from «logical simplicity». Abduction is the most fertile but least secure mode of inference. Its general rationale is inductive: it succeeds often enough and, without it, there is no hope of sufficiently expediting inquiry (often multi-generational) toward new truths.^[121] Coordinative method leads from abducing a plausible hypothesis to judging it for its testability^[23] and for how its trial would economize inquiry itself.^[22] Peirce calls his pragmatism «the logic of abduction».^[122] His pragmatic maxim is: «Consider what effects that might conceivably have practical bearings you conceive the objects of your conception to have. Then, your conception of those effects is the whole of your conception of the object».^[34] His pragmatism is a method of reducing conceptual confusions fruitfully by equating the meaning of any conception with the conceivable practical implications of its object’s conceived effects – a method of experimentational mental reflection hospitable to forming hypotheses and conducive to testing them. It favors efficiency. The hypothesis, being insecure, needs to have practical implications leading at least to mental tests and, in science, lending themselves to scientific tests. A simple but unlikely guess, if uncostly to test for falsity, may belong first in line for testing. A guess is intrinsically worth testing if it has instinctive plausibility or reasoned objective probability, while subjective likelihood, though reasoned, can be misleadingly seductive. Guesses can be chosen for trial strategically, for their caution (for which Peirce gave as an example the game of Twenty Questions), breadth, and incomplexity.^[123] One can hope to discover only that which time would reveal through a learner’s sufficient experience anyway, so the point is to expedite it; the economy of research is what demands the leap, so to speak, of abduction and governs its art.^[22]
2. Deduction. Two stages:
   1. Explication. Unclearly premised, but deductive, analysis of the hypothesis in order to render its parts as clear as possible.
   2. Demonstration: Deductive argumentation, Euclidean in procedure. Explicit deduction of hypothesis’s consequences as predictions, for induction to test, about evidence to be found. Corollarial or, if needed, theorematic.
3. Induction. The long-run validity of the rule of induction is deducible from the principle (presuppositional to reasoning, in general,^[34]) that the real is only the object of the final opinion to which adequate investigation would lead;^[124] anything to which no such process would ever lead would not be real. Induction involving ongoing tests or observations follows a method which, sufficiently persisted in, will diminish its error below any predesignate degree. Three stages:
   1. Classification. Unclearly premised, but inductive, classing of objects of experience under general ideas.
   2. Probation: direct inductive argumentation. Crude (the enumeration of instances) or gradual (new estimate of the proportion of truth in the hypothesis after each test). Gradual induction is qualitative or quantitative; if qualitative, then dependent on weightings of qualities or characters;^[125] if quantitative, then dependent on measurements, or on statistics, or on countings.
   3. Sentential Induction. «… which, by inductive reasonings, appraises the different probations singly, then their combinations, then makes self-appraisal of these very appraisals themselves, and passes final judgment on the whole result».

Invariant explanation

In a 2009 TED talk, Deutsch expounded a criterion for scientific explanation, which is to formulate invariants: «State an explanation [publicly, so that it can be dated and verified by others later] that remains invariant [in the face of apparent change, new information, or unexpected conditions]».^[126]

«A bad explanation is easy to vary.»^{[126]: minute 11:22}

«The search for hard-to-vary explanations is the origin of all progress»^{[126]: minute 15:05}

«That the truth consists of hard-to-vary assertions about reality is the most important fact about the physical world.»^{[126]: minute 16:15}

Invariance as a fundamental aspect of a scientific account of reality had long been part of philosophy of science: for example, Friedel Weinert’s book The Scientist as Philosopher (2004) noted the presence of the theme in many writings from around 1900 onward, such as works by Henri Poincaré (1902), Ernst Cassirer (1920), Max Born (1949 and 1953), Paul Dirac (1958), Olivier Costa de Beauregard (1966), Eugene Wigner (1967), Lawrence Sklar (1974), Michael Friedman (1983), John D. Norton (1992), Nicholas Maxwell (1993), Alan Cook (1994), Alistair Cameron Crombie (1994), Margaret Morrison (1995), Richard Feynman (1997), Robert Nozick (2001), and Tim Maudlin (2002).^[127]

Communication and community

Frequently the scientific method is employed not only by a single person but also by several people cooperating directly or indirectly. Such cooperation can be regarded as an important element of a scientific community. Various standards of scientific methodology are used within such an environment.

Peer review evaluation

Scientific journals use a process of peer review, in which scientists’ manuscripts are submitted by editors of scientific journals to (usually one to three, and usually anonymous) fellow scientists familiar with the field for evaluation. In certain journals, the journal itself selects the referees; while in others (especially journals that are extremely specialized), the manuscript author might recommend referees. The referees may or may not recommend publication, or they might recommend publication with suggested modifications, or sometimes, publication in another journal. This standard is practiced to various degrees by different journals and can have the effect of keeping the literature free of obvious errors and generally improve the quality of the material, especially in the journals that use the standard most rigorously. The peer-review process can have limitations when considering research outside the conventional scientific paradigm: problems of «groupthink» can interfere with open and fair deliberation of some new research.^[128]

Documentation and replication

Sometimes experimenters may make systematic errors during their experiments, veer from standard methods and practices (Pathological science) for various reasons, or, in rare cases, deliberately report false results. Occasionally because of this then, other scientists might attempt to repeat the experiments to duplicate the results.

Archiving

Researchers sometimes practice scientific data archiving, such as in compliance with the policies of government funding agencies and scientific journals. In these cases, detailed records of their experimental procedures, raw data, statistical analyses, and source code can be preserved to provide evidence of the methodology and practice of the procedure and assist in any potential future attempts to reproduce the result. These procedural records may also assist in the conception of new experiments to test the hypothesis, and may prove useful to engineers who might examine the potential practical applications of a discovery.

Data sharing

When additional information is needed before a study can be reproduced, the author of the study might be asked to provide it. They might provide it, or if the author refuses to share data, appeals can be made to the journal editors who published the study or to the institution which funded the research.

Limitations

Since a scientist can’t record everything that took place in an experiment, facts selected for their apparent relevance are reported. This may lead, unavoidably, to problems later if some supposedly irrelevant feature is questioned. For example, Heinrich Hertz did not report the size of the room used to test Maxwell’s equations, which later turned out to account for a small deviation in the results. The problem is that parts of the theory itself need to be assumed to select and report the experimental conditions. The observations are hence sometimes described as being ‘theory-laden’.

Science of complex systems

Science applied to complex systems can involve elements such as transdisciplinarity, systems theory, control theory, and scientific modelling. The Santa Fe Institute studies such systems;^[76] Murray Gell-Mann interconnects these topics with message passing.^[129][20]

Some biological systems, such those involved in proprioception, have been fruitfully modeled by engineering techniques.^[130][131]

In general, the scientific method may be difficult to apply stringently to diverse, interconnected systems and large data sets. In particular, practices used within Big data, such as predictive analytics, may be considered to be at odds with the scientific method,^[132] as some of the data may have been stripped of the parameters which might be material in alternative hypotheses for an explanation; thus the stripped data would only serve to support the null hypothesis in the predictive analytics application. Fleck 1979, pp. 38–50 notes «a scientific discovery remains incomplete without considerations of the social practices that condition it».^[133]

Philosophy and sociology of science

Analytical philosophy

Philosophy of science looks at the underpinning logic of the scientific method, at what separates science from non-science, and the ethic that is implicit in science. There are basic assumptions, derived from philosophy by at least one prominent scientist,^[C][134] that form the base of the scientific method – namely, that reality is objective and consistent, that humans have the capacity to perceive reality accurately, and that rational explanations exist for elements of the real world.^[134] These assumptions from methodological naturalism form a basis on which science may be grounded. Logical positivist, empiricist, falsificationist, and other theories have criticized these assumptions and given alternative accounts of the logic of science, but each has also itself been criticized.

Thomas Kuhn examined the history of science in his The Structure of Scientific Revolutions, and found that the actual method used by scientists differed dramatically from the then-espoused method. His observations of science practice are essentially sociological and do not speak to how science is or can be practiced in other times and other cultures.

Norwood Russell Hanson, Imre Lakatos and Thomas Kuhn have done extensive work on the «theory-laden» character of observation. Hanson (1958) first coined the term for the idea that all observation is dependent on the conceptual framework of the observer, using the concept of gestalt to show how preconceptions can affect both observation and description.^[135] He opens Chapter 1 with a discussion of the Golgi bodies and their initial rejection as an artefact of staining technique, and a discussion of Brahe and Kepler observing the dawn and seeing a «different» sunrise despite the same physiological phenomenon.^[i][ab] Kuhn^[136] and Feyerabend^[137] acknowledge the pioneering significance of Hanson’s work.

Kuhn said^{[Propose striking this paragraph as inconsistent with the article.]} the scientist generally has a theory in mind before designing and undertaking experiments to make empirical observations, and that the «route from theory to measurement can almost never be traveled backward». For Kuhn, this implies that how theory is tested is dictated by the nature of the theory itself, which led Kuhn to argue that «once it has been adopted by a profession … no theory is recognized to be testable by any quantitative tests that it has not already passed» (revealing Kuhn’s rationalist thinking style).^[138]

Post-modernism and science wars

Paul Feyerabend similarly examined the history of science, and was led to deny that science is genuinely a methodological process. In his book Against Method he argues that scientific progress is not the result of applying any particular method. In essence, he says that for any specific method or norm of science, one can find a historic episode where violating it has contributed to the progress of science. Thus, if believers in the scientific method wish to express a single universally valid rule, Feyerabend jokingly suggests, it should be ‘anything goes’.^[139] However, this is uneconomic.^[B] Criticisms such as Feyerabend’s led to the strong programme, a radical approach to the sociology of science.

The postmodernist critiques of science have themselves been the subject of intense controversy. This ongoing debate, known as the science wars, is the result of conflicting values and assumptions between the postmodernist and realist camps. Whereas postmodernists assert that scientific knowledge is simply another discourse (this term has special meaning in this context) and not representative of any form of fundamental truth, realists in the scientific community maintain that scientific knowledge does reveal real and fundamental truths about reality. Many books have been written by scientists which take on this problem and challenge the assertions of the postmodernists while defending science as a legitimate method of deriving truth.^[140]

Anthropology and sociology

In anthropology and sociology, following the field research in an academic scientific laboratory by Latour and Woolgar, Karin Knorr Cetina has conducted a comparative study of two scientific fields (namely high energy physics and molecular biology) to conclude that the epistemic practices and reasonings within both scientific communities are different enough to introduce the concept of «epistemic cultures», in contradiction with the idea that a so-called «scientific method» is unique and a unifying concept.^[141] Comparing ‘epistemic cultures’ with Fleck 1935, Thought collectives, (denkkollektiven): Entstehung und Entwicklung einer wissenschaftlichen Tatsache: Einfǖhrung in die Lehre vom Denkstil und Denkkollektiv^[142]
Fleck 1979, p. xxvii recognizes that facts have lifetimes, flourishing only after incubation periods. His selected question for investigation (1934) was «HOW, THEN, DID THIS EMPIRICAL FACT ORIGINATE AND IN WHAT DOES IT CONSIST?».^[143] But by Fleck 1979, p.27, the thought collectives within the respective fields will have to settle on common specialized terminology, publish their results and further intercommunicate with their colleagues using the common terminology, in order to progress.^[144]

See: Cognitive revolution, Psychology and neuroscience

Relationship with mathematics

Science is the process of gathering, comparing, and evaluating proposed models against observables. A model can be a simulation, mathematical or chemical formula, or set of proposed steps. Science is like mathematics in that researchers in both disciplines try to distinguish what is known from what is unknown at each stage of discovery. Models, in both science and mathematics, need to be internally consistent and also ought to be falsifiable (capable of disproof). In mathematics, a statement need not yet be proved; at such a stage, that statement would be called a conjecture. But when a statement has attained mathematical proof, that statement gains a kind of immortality which is highly prized by mathematicians, and for which some mathematicians devote their lives.^[145]

Mathematical work and scientific work can inspire each other.^[33] For example, the technical concept of time arose in science, and timelessness was a hallmark of a mathematical topic. But today, the Poincaré conjecture has been proved using time as a mathematical concept in which objects can flow (see Ricci flow).

Nevertheless, the connection between mathematics and reality (and so science to the extent it describes reality) remains obscure. Eugene Wigner’s paper, The Unreasonable Effectiveness of Mathematics in the Natural Sciences, is a very well-known account of the issue from a Nobel Prize-winning physicist. In fact, some observers (including some well-known mathematicians such as Gregory Chaitin, and others such as Lakoff and Núñez) have suggested that mathematics is the result of practitioner bias and human limitation (including cultural ones), somewhat like the post-modernist view of science.

George Pólya’s work on problem solving,^[146] the construction of mathematical proofs, and heuristic^[147][148] show that the mathematical method and the scientific method differ in detail, while nevertheless resembling each other in using iterative or recursive steps.

	Mathematical method	Scientific method
1	Understanding	Characterization from experience and observation
2	Analysis	Hypothesis: a proposed explanation
3	Synthesis	Deduction: prediction from the hypothesis
4	Review/Extend	Test and experiment

In Pólya’s view, understanding involves restating unfamiliar definitions in your own words, resorting to geometrical figures, and questioning what we know and do not know already; analysis, which Pólya takes from Pappus,^[149] involves free and heuristic construction of plausible arguments, working backward from the goal, and devising a plan for constructing the proof; synthesis is the strict Euclidean exposition of step-by-step details^[150] of the proof; review involves reconsidering and re-examining the result and the path taken to it.

Building on Pólya’s work, Imre Lakatos argued that mathematicians actually use contradiction, criticism, and revision as principles for improving their work.^[151][ac] In like manner to science, where truth is sought, but certainty is not found, in Proofs and Refutations, what Lakatos tried to establish was that no theorem of informal mathematics is final or perfect. This means that we should not think that a theorem is ultimately true, only that no counterexample has yet been found. Once a counterexample, i.e. an entity contradicting/not explained by the theorem is found, we adjust the theorem, possibly extending the domain of its validity. This is a continuous way our knowledge accumulates, through the logic and process of proofs and refutations. (However, if axioms are given for a branch of mathematics, this creates a logical system —Wittgenstein 1921 Tractatus Logico-Philosophicus 5.13; Lakatos claimed that proofs from such a system were tautological, i.e. internally logically true, by rewriting forms, as shown by Poincaré, who demonstrated the technique of transforming tautologically true forms (viz. the Euler characteristic) into or out of forms from homology,^[152] or more abstractly, from homological algebra.^[153])^[154][ac]

Lakatos proposed an account of mathematical knowledge based on Polya’s idea of heuristics. In Proofs and Refutations, Lakatos gave several basic rules for finding proofs and counterexamples to conjectures. He thought that mathematical ‘thought experiments’ are a valid way to discover mathematical conjectures and proofs.^[156]

Gauss, when asked how he came about his theorems, once replied «durch planmässiges Tattonieren» (through systematic palpable experimentation).^[157]

Relationship with statistics

When the scientific method employs statistics as a key part of its arsenal, there are mathematical and practical issues that can have a deleterious effect on the reliability of the output of scientific methods. This is described in a popular 2005 scientific paper «Why Most Published Research Findings Are False» by John Ioannidis, which is considered foundational to the field of metascience.^[158] Much research in metascience seeks to identify poor use of statistics and improve its use.^[ad][m] See Preregistration (science)#Rationale

The particular points raised are statistical («The smaller the studies conducted in a scientific field, the less likely the research findings are to be true» and «The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true.») and economical («The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true» and «The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true.») Hence: «Most research findings are false for most research designs and for most fields» and «As shown, the majority of modern biomedical research is operating in areas with very low pre- and poststudy probability for true findings.» However: «Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds,» which means that *new* discoveries will come from research that, when that research started, had low or very low odds (a low or very low chance) of succeeding. Hence, if the scientific method is used to expand the frontiers of knowledge, research into areas that are outside the mainstream will yield the newest discoveries. See: Expected value of sample information, False positives and false negatives, Test statistic, and Type I and type II errors

Role of chance in discovery

Somewhere between 33% and 50% of all scientific discoveries are estimated to have been stumbled upon, rather than sought out. This may explain why scientists so often express that they were lucky.^[159] Louis Pasteur is credited with the famous saying that «Luck favours the prepared mind», but some psychologists have begun to study what it means to be ‘prepared for luck’ in the scientific context. Research is showing that scientists are taught various heuristics that tend to harness chance and the unexpected.^[159][160] This is what Nassim Nicholas Taleb calls «Anti-fragility»; while some systems of investigation are fragile in the face of human error, human bias, and randomness, the scientific method is more than resistant or tough – it actually benefits from such randomness in many ways (it is anti-fragile). Taleb believes that the more anti-fragile the system, the more it will flourish in the real world.^[50]

Psychologist Kevin Dunbar says the process of discovery often starts with researchers finding bugs in their experiments. These unexpected results lead researchers to try to fix what they think is an error in their method. Eventually, the researcher decides the error is too persistent and systematic to be a coincidence. The highly controlled, cautious, and curious aspects of the scientific method are thus what make it well suited for identifying such persistent systematic errors. At this point, the researcher will begin to think of theoretical explanations for the error, often seeking the help of colleagues across different domains of expertise.^[159][160]

See also

Problems and issues

History, philosophy, sociology

Notes

Notes: Problem-solving via scientific method

References

Sources

Further reading

External links

• Andersen, Anne; Hepburn, Brian. «Scientific Method». In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
• «Confirmation and Induction». Internet Encyclopedia of Philosophy.
• Scientific method at PhilPapers
• Scientific method at the Indiana Philosophy Ontology Project
• An Introduction to Science: Scientific Thinking and a scientific method by Steven D. Schafersman.
• Introduction to the scientific method at the University of Rochester
• The scientific method from a philosophical perspective
• Theory-ladenness by Paul Newall at The Galilean Library
• Lecture on Scientific Method by Greg Anderson
• Using the scientific method for designing science fair projects
• Scientific Methods an online book by Richard D. Jarrard
• Richard Feynman on the Key to Science (one minute, three seconds), from the Cornell Lectures.
• Lectures on the Scientific Method by Nick Josh Karean, Kevin Padian, Michael Shermer and Richard Dawkins
• «How Do We Know What Is True?» (animated video; 2:52)

Last Updated:
6 Apr 2023
— Updated example sentences

Definition

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 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).

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 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!

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.

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.

Quiz

Since the 17^th century, the scientific method has been the gold standard for investigating the natural world. It is how scientists correctly arrive at new knowledge, and update their previous knowledge. It consists of systematic observation, measurement, experiment, and the formulation of questions or hypotheses.

Discover 14 more articles on this topic

1. Formulate Question/Hypothesis

1. Define the Research Question
2. Review the Literature
3. Create a Hypothesis

2. Collect Data

1. Preparation: Make the Hypothesis Testable (Operationalization)
2. Preparation: Design the Study
3. Conduct the Experiment orObservation

3. Test Hypothesis

1. Organize the Data
2. Analyse the Results
3. Check if the Results Support or disprove the Hypothesis

4. Conclusion

1. Look for Other Possible Explanations
2. Generalize to the Real World
3. Suggestions to Further Research

What is the best way to uncover objective truths about the world we live in?

Why do we accept some ideas as objective fact while others are more up for debate?

The answer is the scientific method.

Imagine that you are living in the 1600s, and wonder why you sometimes see maggots in decaying food. Where do they come from?

You may ask around and discover that the experts of the day believe living organisms sometimes emerge from inanimate materials. In fact, this theory, called “spontaneous generation”, was popular for around 2000 years, and famously expounded by Aristotle. However, it was wrong.

It was not till 1859 that Louis Pasteur’s important experiment disproved this theory and paved the way for a better one, the theory of “biogenesis”, which is still subscribed to today.

Simply making an observation and formulating a possible explanation is not scientific.

Instead, the scientific method is a comprehensive process that ensures that scientists have the best chance of arriving at the objective truth about a phenomenon. Pasteur had a question, formulated a hypothesis, devised a sound experiment to test it, then applied logic to interpret his results.

Empirical vs. Rational

How do you know that the sky is blue?

Because you can see it!

Observation is humankind’s most natural method of gathering data about the world, and we do this via sensory experience, for example sight.

Empirical observation is the gathering of data using only information that is directly or indirectly available to our senses.

Empirical observation is the foundation of any experiment, and so forms a crucial part of the scientific method.

What characterizes empirical evidence is that it uses objective observable data, as opposed to opinion or anecdote, to concisely answer a research question. Empirical evidence is always the same, regardless of who the observer is. For example, anybody can look at a thermometer and observe that it reads 10 °C, but many different observers may stand in a room and claim it’s “very cold” or “only somewhat cold.” The former is an empirical observation, the latter is simply opinion.

Science relies heavily on observation and measurement, and the vast majority of research involves some type of practical experimentation.

This can be anything from measuring the Doppler Shift of a distant galaxy to handing out questionnaires in a shopping center and observing the kinds of responses you get.

The kind of knowledge you arrive at this way is termed a posteriori, meaning it’s gained after experience.

In contrast, a different kind of knowledge is a priori, which means it comes before, or independent of any observation.

The rationalist approach to gathering knowledge says that truth can be found by reasoning and argument alone.

Such knowledge is inductive and developed from first principles. Though this approach to gaining knowledge is invaluable, it is empirical research and the rigors of the scientific method that are most expected in true scientific research.

Induction vs. Deduction

The scientific method doesn’t end after the results are obtained. Collecting, analyzing, interpreting and integrating data is part of the process.

In other words, how can the findings be related to what is already known?

The logic behind research design is the framework that allows us to make meaning of the results we’ve observed. Scientists must pay careful attention to the reasoning behind their methods if they wish to make meaningful analyses of results. There are two main methods of reasoning:

Deductive: Ending up at a conclusion based on the inherent logic of an argument. Moves from the more general to the more specific.

Inductive: Ending up at a conclusion based on a set of observations. The conclusion can be either more or less probable, based on the strength of the evidence. More evidence can always be gathered. Moves from the more specific to the more general.

You may notice that the first corresponds to a rational approach, and the second to an empirical approach.

Consider deductive reasoning and the following argument:

• All men are mortal.
• Sherlock Holmes is a man.
• Therefore, Sherlock Holmes is mortal.

Provided that each premise above is true, we know for certain that the conclusion is true. This has nothing to do with our knowledge of men, or Sherlock Holmes. We simply need to look at the inherent logic of the argument to see that the conclusion is true.

Consider another example. When Sherlock Holmes gathers clues about a mystery, he may form a theory about what happened. Though he can never prove his theory, the more clues he has and the stronger his evidence, the more probable his theory is likely to be. He is using inductive reasoning.

Now, because scientific research favors empirical evidence and the scientific method, it also favors inductive reasoning. This means that whenever research is conducted, it never proves or disproves something once and for all, but only adds evidence in support of an inductive argument. The stronger our specific evidence, the stronger generalizations we can make.

This process of induction and generalization allows scientists to make predictions about how they think that something should behave, and design an experiment to test it.

The Scientific Method Relies on Data

The scientific method formalizes its observation by taking measurements, analyzing the results, then feeding these findings back into theories of what we know about the world. There are two major kinds of data: quantitative and qualitative.

Quantitative data measures an aspect of the real world in quantifiable terms, i.e. numbers. Qualitative data observes the qualities of natural phenomena, such as opinions and motivations.

For example, opinions about the beauty of a particular human face is qualitative data, while data about the distance in millimeters between various facial features is quantitative.

Quantitative measurements are generally associated with what are known as ‘hard’ sciences, such as physics, chemistry and astronomy. They can be gained through experimentation or through observation.

For Example:

• At the end of the experiment, 50% of the bacteria in the sample treated with penicillin were left alive.
• The experiment showed that the moon is 384403 km away from the earth.
• The pH of the solution was 7.1.

As a rule of thumb, a quantitative measurement usually has an SI or SI derived unit. Percentages and numbers fall into this category.

Qualitative measurements are more associated with ‘softer’ or social sciences. However, such data can undergo numerical manipulation or scaling to transform it into quantitative data.

As an example, a social scientist may open-endedly interview drug addicts in a series of case studies to give a deeper understanding of their day to day lives. The data gathered gives a richer knowledge of the aspects of drug use that numbers may not be able to capture.

For Example:

• The researcher noted recurrent themes of alienation in subjects’ responses.
• Participants had an external locus of control and a generally pessimistic outlook.

However, if the social scientist performs some sort of manipulation on this data, such as devising a numerical scale to assess pessimism via response to specific questions, then he generates quantitative results.

For Example:

• On average, the subjects showed an anxiety level of four.
• 91% of respondents stated that they preferred Hershey bars.

Measuring anxiety, preference, pain and aggression on scales are some examples of qualitative concepts measured quantitatively.

Both types of data are extremely important for understanding the world around us and the majority of scientists use both types of data.

A medical researcher might design experiments to test the effectiveness of a drug, using a placebo to contrast. However, she might also perform in depth case studies on a few of the subjects to assess their personal experiences taking the medication.

Qualitative research can go hand in hand with quantitative, for example a qualitative study can later suggest research questions for further quantitative research, or vice versa.

Systematic and Methodical

Scientists are conservative in how they approach results and they are naturally skeptical.

It takes more than one experiment to change the way they think, however convincing the headlines are, and any results must be retested and repeated until a solid body of evidence is built up. This process ensures that researchers do not make mistakes or purposefully manipulate evidence.

Over time, the scientific method can improve on even the most accepted theories, or bring into being completely new ones. This is called a paradigm shift, and is an integral part of the scientific method. Most groundbreaking research, such as Einstein’s Relativity or Mendel’s Genetics, causes a titanic shift in the prevailing scientific thought.

Summary

The scientific method has evolved, over many centuries, to ensure that scientists make meaningful discoveries, founded upon logical hypotheses that can be tested empirically.

The exact process varies between scientific disciplines, but they all follow the above principle of observe — predict — test — generalize.

How Strictly Does Science Follow These Rules?

Apart from a few strictly defined physical sciences, most scientific disciplines have to bend and adapt these rules, especially sciences involving the unpredictability of natural organisms and humans.

Any scientist should have a good understanding of the underlying principles. If you are going to bend and adapt the rules, you need to understand the rules in the first place.

The scientific method is an empirical process used to acquire scientific knowledge. It is broadly applied to various sciences and enables the testing and validation of a scientific hypothesis. The problem is defined based on various observations. For example, a question can arise from the observation of a natural phenomenon. This question can lead to the formulation of a hypothesis and predictions. These can be tested by collecting data using the appropriate methodology. The final steps of the scientific method include data analysis and validation of the hypothesis. Altogether, the conclusions drawn from the scientific method will lead to new questions. This will ultimately improve our knowledge towards a better comprehension of the world surrounding us.

When was the Scientific Method Invented? Who Invented the Scientific Method?

Even though various scientific methodologies were elaborated in ancient Egypt and Babylonia, the inventor of the scientific method is usually considered to be Aristotle¹. This antique Greek philosopher introduced empiricism to science in his text Posterior Analytics². In other words, empiricism means that our scientific knowledge must be based on observations and empirical evidence. This is a key concept of the scientific method. The term “scientific method” became popular much later during the 19^th and 20^th centuries when it was broadly introduced into dictionaries and encyclopedias³. 

What are the Steps of the Scientific Method?

What is the First Step of the Scientific Method?

Step 1- What is a Scientific Question and How to Use the Scientific Method?

First of all, the scientific method begins with a question, something that needs to be answered. This problem can arise from initial observations leading to a specific question, which would ideally be something that you can measure or quantify. This initial question will later lead to the formulation of the working hypothesis.

What is the Second Step of the Scientific Method?

Step 2- Literature
search

Before performing scientific experiments in a laboratory, every scientist will begin his research by doing an extensive literature search. This is a crucial step of the scientific method because it will reveal what is already known about the problem. The idea is to see if anything relevant to the question is already known. In addition, the literature search can be used to determine the appropriate methodology to address the question.

What is the Third Step of the Scientific Method?

Step 3- Formulation
of the hypothesis and predictions

Following extensive background research, the scientist can then formulate the hypothesis. It is a plausible assumption based on the scientific knowledge and the methodology available. The scientist can then predict the possible outcome before performing any experiments. For example, a scientist will formulate the hypothesis that if he changes the parameter or variable X, it could result in different effects (A, B, or C).

What is the Fourth Step of the Scientific Method?

Step 4- Experimental design, scientific experiment, and data collection

Obviously, experiments are an important part of the scientific method. Every rigorous scientific experiment needs to be performed using the appropriate methodology. For instance, the instrument used to test the hypothesis must be accurate and efficient. In order to be valid, the experiment must be performed along with appropriate control groups and in controlled conditions to assess the effect of a single parameter at a time. Furthermore, the scientist must take into account all the factors that can introduce a bias during data collection. The experiment also needs to be reproduced a few times to make sure that the results are reproducible and are not obtained randomly. Finally, different methodologies can be used to test the same hypothesis, therefore strengthening the validity of the scientific findings.

What is the Fifth Step of the Scientific Method?

Step 5-
Data analysis

Once data collection is over, the scientist can proceed to its analysis. The collected data can be presented in different ways such as pictures, schemas, videos, etc. If numerical data was obtained, it can be presented in a chart. The type of chart selected for graphical representations depends on the type of question. For example, proportions are easily represented in a pie chart whereas a bar chart will be better suited to show the evolution of monthly sales of a company through the years. In addition, the scientist can perform various mathematical equations and statistical analyses to further characterize his dataset.

What is the Sixth Step of the Scientific Method?

Step 6-
Hypothesis validation or invalidation, and formulation of new related questions

It is now time to draw conclusions about the initial question. The data collected and analyzed can either validate or invalidate the hypothesis. When drawing conclusions, the scientist must be critical regarding the quality of the data obtained and he should also consider the limitations of the methodology used for testing. Often, the conclusions will lead to additional questions and the formulation of new hypotheses.

What is the Seventh Step of the Scientific Method?

Step 7- Sharing
the scientific discoveries: publication and peer review

Someone could easily become an improvised scientist and apply the scientific method to validate or invalidate his own hypothesis. However, what makes the strength of the scientific method is to share the knowledge gained from a scientific experiment that was performed. This way, the scientific community can benefit from the work of others before establishing their own hypotheses. Every research project published therefore contribute to broader scientific advances, even when the initial hypothesis was proven wrong.  In addition, our comprehension of a specific scientific topic is constantly evolving as it can be either validated or even sometimes challenged by the completion of more advanced research projects.

The scientific method is a cornerstone of science and this is why it is important to teach it to kids. This concept is generally taught to children during the 4^th, 5^th, or 6^th grade. The scientific method can help these kids to develop critical thinking and to give them the tools required to solve complex problems.

How to Use the Scientific Method and How to Design an Experiment Using the Scientific Method? An Example Applied to Drug Discovery

The
scientific method can be applied to answer various questions related to
biology, psychology, sociology, etc. Here, we have already explained all the
steps constituting the scientific method and their respective order. Let’s now
see a fictional example to show how the scientific method can be applied to
solve complex problems in the pharmaceutical industry.

Step1: What is a Scientific Question?

Let’s say that a chemist is looking for new drugs that could be used in the pharmaceutical industry. The initial question could be something like “Is there a better treatment to control the blood pressure of patients?”. This is a good example showing how the rigorous application of the scientific method can answer a complex question.

Step 2:
Literature research

The scientist will then proceed to an extensive literature search and gather all the information available for the active molecules already used as treatments. During his research, the chemist noticed a molecule that could be chemically transformed to alter its structure. In addition, the structure of the original molecule is available, and bio-informatics analysis indicates that the modification would occur in the active site of the molecule.

Step 3: What is an Example of a Hypothesis, How to Write a Hypothesis, and What is a Prediction in Science?

The scientist, therefore, emits the hypothesis that this modification could increase the efficiency of the treatment. He then predicts that the modification of the molecule will increase its binding to receptors located on the surface of blood vessels and that it will reduce blood pressure and side effects.

Step 4:
Experiment and data collection

In Vitro Experiments

The scientist decides to first test his hypothesis by measuring how the alteration of the active molecule can affect its capacity to bind the receptor. He will use purified molecules from either the original formula or the altered version of the molecule. Then, he will measure the binding capacity of the molecules towards their target receptor in a test tube.

In Vivo Experiments

To assess the biological properties of the newly identified molecule, the scientist will next use animals to analyze how the molecule can affect a complex organism such as rats. This is a complex experiment that needs to be designed properly in order to draw the right conclusions. The scientist decides to use obese rats that are prone to high blood pressure to test the efficiency of his new drug. Three groups will be monitored. The first group will be obese rats receiving no treatment at all. The second will contain animals receiving the original form of the molecule whereas the third will be administered the new molecule.

The experiment must be performed in controlled conditions

In order to be valid, the experiment needs to be performed in controlled conditions. To consider additional factors that might introduce a bias during data analysis, the groups compared must be homogeneous. Many factors can influence data interpretation and to make sure to draw the right conclusions, the scientist decides to use only male rats of approximately the same age. The blood pressure of these animals will then be monitored over the weeks and blood samples will be taken to reveal changes in its content.

Step 5:
Data analysis

The results
obtained during data collection can be presented in various graphical
representations. For instance, the strength of the binding exhibited by these
different molecules can be easily compared in a simple bar chart. The blood
pressure measurements for each group can be presented as a function of time
since the beginning of the treatment in a scatter plot. In addition, a trend
line or regression line can be drawn on the graph to emphasize the various
trends exhibited by each group of animals.

Step 6:
Validation of the hypothesis

Once the different scientific experiments are performed, the scientist will be able to re-examine the initial hypothesis. If the methodology was appropriate and the influence of external factors was reduced to a minimum, the scientist will then be able to use his data and analysis to validate or invalidate his initial hypothesis.

In this example, the scientist will conclude that the modification of an existing molecule used to regulate blood pressure can increase its efficiency in comparison with the original drug. However, a major limitation of this study is that it was performed on an animal model. One could therefore ask if this newly identified molecule would be equally efficient on human patients. As you can see, the application of the scientific method for this research raised another important question, which can then be addressed by other scientists.

Step 7:
Publication and peer review

In order to benefit the entire scientific community, a scientist must publish his findings. First, the scientist will first write an article summarizing his research project. He can then submit his article to a scientific journal where it will be reviewed by peers to ensure the quality of the results before their publication. Once the results are published, they can be accessible to the whole scientific community and can be cited in the work of other scientists. Altogether, this process allows the expansion of knowledge in a particular scientific field.

The Scientific Method – A Short Quiz

Question 1:
Classify these steps of the scientific method in the right order

1. Experiment
2. Literature search
3. Ask a question
4. Publication
5. Data analysis
6. Validation of the hypothesis
7. Formulation of the hypothesis and predictions

A) 2-3-7-1-5-6-4

B) 3-2-7-1-5-6-4

C) 3-2-7-1-5-4-6

D) 2-3-7-1-5-4-6

Question 2: To be able to draw valid conclusions, a scientist must use a methodology that…

1. Generate reproducible data
2. Can appropriately test the
   hypothesis
3. Is precise enough to distinguish
   between conditions
4. Is performed in a controlled
   environment

A) 1 and 2

B) 1, 2 and 3

C) 2, 3 and 4

D) 1, 2, 3 and 4

Question 3: True or false. A scientific study is invalid and cannot be published if the hypothesis was wrong.

A) True

B) False   

Answers

1B, 2D, 3B

Now that you know the different steps of the scientific method, what do you think about this reasoning process? Don’t be shy and share your thoughts with us in the comment section below!

Check my previous post to see how to experiment with light refraction through a prism!

References

1- Wikipedia – The history of the scientific method

2- Aristotle, considered the inventor of the scientific methods – Posterior Analytics

3- Wikipedia – Scientific method

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