The scientific method is the set of procedures scientists use to gain reliable knowledge about the natural world. These procedures center on careful observation, testable hypotheses, controlled experiments and the open review of results. Textbooks often present it as a fixed series of steps. Historians and philosophers of science mostly describe it differently: as a group of related practices that vary between disciplines. What these practices share is a commitment to empirical evidence, logical reasoning and the revision of claims when new data appear. The method underlies modern physics, chemistry, biology and medicine. It is a central topic in epistemology and the philosophy of science.
Core elements
A common textbook version runs as follows. A researcher observes a phenomenon and asks a question about it. The researcher then proposes a hypothesis, which is a tentative explanation. From the hypothesis the researcher derives predictions and tests them by experiment or further observation. Finally, the results are analyzed and the hypothesis is accepted, revised or rejected. In practice these steps overlap and repeat. One study's results usually lead to new questions.
Several ideas recur across fields:
- Empiricism. Claims must ultimately answer to observation and measurement, not to authority or intuition alone.
- Testability. A hypothesis must make predictions that could turn out to be wrong.
- Control of variables. Experiments change one factor at a time while holding others constant. Researchers also compare treated groups with control groups.
- Reproducibility. Independent researchers following the same procedures should get similar results.
- Quantification. Measurements and statistical analysis express uncertainty and separate real effects from chance. They draw on the mathematics of probability.
- Peer scrutiny. Results are shared with other experts, often through peer review, who check them for errors and bias.
Forms of reasoning
Scientific reasoning combines several kinds of inference. Inductive reasoning moves from particular observations to general laws. Deductive reasoning, formalized in logic, derives specific predictions from general premises. In the hypothetico-deductive model, a researcher proposes a hypothesis, deduces what should follow from it and checks those consequences against experience. The American philosopher Charles Sanders Peirce added a third type, abduction: inference to the most plausible explanation of surprising facts. All three differ from mathematical proof. Empirical conclusions stay provisional and can be revised in light of further evidence.
Historical development
People have made systematic observations of nature since antiquity. Aristotle in ancient Greece stressed close study of nature and classification. However, his account of scientific knowledge relied heavily on deduction from first principles. During the Islamic Golden Age, Ibn al-Haytham wrote the Book of Optics in the early 11th century. It combined optical theory with controlled experiments on light and vision, and it is often cited as an early model of experimental inquiry. In 13th-century England, the Franciscan friar Roger Bacon championed what he called scientia experimentalis (experimental knowledge) in his Opus Majus of 1267.
The method took more recognizable form during the Scientific Revolution. Francis Bacon published the Novum Organum in 1620. He argued that knowledge should be built by induction from systematic observation and experiment. He also warned against the "idols of the mind," meaning sources of bias that distort human judgment. Galileo Galilei joined mathematical description with experiment. René Descartes stressed methodical doubt and deduction. Isaac Newton's Principia (1687) included "Rules of Reasoning in Philosophy," and its success made the combination of mathematical theory and empirical testing a model for other sciences. Later work such as Antoine Lavoisier's quantitative chemistry and Charles Darwin's theory of natural selection extended these methods to new fields.
Philosophical debates
In the 18th century, David Hume raised the problem of induction. No finite number of observations can logically guarantee a general law. In the 20th century, Karl Popper answered that scientific theories can never be conclusively verified, only falsified. He proposed falsifiability as the criterion that separates science from non-science, a question known as the demarcation problem. Popper often cited Albert Einstein's general relativity as an example. The theory made risky predictions, such as the bending of starlight by the Sun, which observations during the 1919 solar eclipse could have refuted.
Thomas Kuhn's The Structure of Scientific Revolutions (1962) put more weight on history. Kuhn argued that "normal science" works within a shared framework, which he called a paradigm. Paradigms are replaced in occasional revolutions, and that process is not purely logical. Imre Lakatos proposed judging whole "research programmes" by whether they keep making successful predictions. Paul Feyerabend's Against Method (1975) argued that no single set of rules describes how science has actually advanced. Bayesian approaches, by contrast, treat scientific reasoning as updating degrees of belief by the rules of probability. Many philosophers today accept that no single algorithm defines science. They still regard evidence, testing and criticism as its core.
Variation across disciplines
Methods differ widely from field to field. Laboratory sciences such as chemistry and particle physics can run tightly controlled experiments. Observational sciences such as astrophysics, paleontology and ecology often cannot manipulate their subjects. Instead they rely on natural experiments, comparison and inference from many independent lines of evidence. Clinical research uses the randomized controlled trial, often with blinding and placebo controls, to reduce bias when testing treatments. Computer simulation and the analysis of very large datasets, including methods from machine learning, have become further tools for forming and testing hypotheses.
Limitations and reform
The scientific method does not remove error. It aims to find and correct error over time. Known weaknesses include confirmation bias, small sample sizes and selective reporting of positive results. Another is misuse of statistical significance testing. In the 2010s, large replication projects in psychology, biomedicine and other fields found that many published findings could not be reproduced. This problem became known as the replication crisis. Proposed responses include preregistering study designs before data collection, publishing data and code openly, using larger samples and running more direct replications. These reforms rest on the same principle as the method itself: scientific claims gain credibility by surviving independent and repeated testing.
References
- Search for Truth: A Brief History of the Scientific Methodbrewminate.com
- Who Invented the Scientific Method? - Kronecker Walliskroneckerwallis.com
- Novum Organumen.wikipedia.org
- THE SCIENTIFIC METHODcalteches.library.caltech.edu
- Trim Size: 170mm x 244mm Cautin ecp0388.tex V1 - 05/27/2014 12:55 A.M. Page 1philarchive.org
- On the Nature of Sciencearxiv.org