research topics in philosophy of science

Philosophy of Science Research Paper Topics

This page provides a comprehensive list of philosophy of science research paper topics , designed to usher students into the vast realm of the interplay between philosophy and scientific inquiry. As the study of philosophy of science continues to evolve, there’s an increasing need for students to delve deeply into its multifaceted avenues, understanding not just the foundational principles but also the emerging debates and discussions. From examining the underlying assumptions that drive scientific research to scrutinizing the ethical dimensions of modern scientific practices, the philosophy of science offers a myriad of avenues for intellectual exploration. This list will serve as both a starting point for novices and a deep dive for those already familiar with some aspects of the field, ensuring that every student can find a topic tailored to their interests and academic goals.

100 Philosophy of Science Research Paper Topics

In the quest to fathom the universe and our place within it, humanity has leaned on both science and philosophy as guiding lights. The philosophy of science, as a discipline, dives deep into the analysis of scientific practice and the conceptual foundations of science. It critically examines the nature of scientific knowledge, the practice of scientific inquiry, and the interplay of science with other societal elements. For students looking to understand the broader context in which scientific theories arise, evolve, and sometimes fade away, exploring philosophy of science research paper topics offers invaluable insights.

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• Logic and Scientific Reasoning.
• Induction, Deduction, and Abduction in Science.
• Falsifiability as a Criterion for Scientific Theory.
• The Role of Observation and Experimentation.
• Theory-ladenness of Observation.
• The Duhem-Quine Thesis.
• Confirmation and Empirical Content.
• Science and Pseudoscience: Demarcation Problem.
• The Underdetermination of Theories.
• Models and Analogies in Science.
• Presocratic Natural Philosophers.
• Aristotelian Science.
• The Scientific Renaissance.
• Logical Positivism and Logical Empiricism.
• Popper’s Critical Rationalism.
• Kuhn’s Structure of Scientific Revolutions.
• Lakatos and Research Programs.
• Feyerabend’s Epistemological Anarchy.
• The Evolution of Scientific Explanation.
• The Emergence of Quantum Mechanics and its Philosophical Implications.
• The Copernican Revolution.
• The Darwinian Revolution.
• Einstein’s Relativity and its Impact.
• Quantum Mechanics: A New Worldview.
• The Double Helix and the New Biology.
• Shifts in Geoscience: From Geocentrism to Plate Tectonics.
• The Rise of Systems Biology.
• The Cognitive Revolution in Psychology.
• Big Bang Theory: A Cosmological Revolution.
• The Digital Revolution and Computational Sciences.
• The Debate on Scientific Realism.
• Arguments for and against Anti-realism.
• Instrumentalism: A Middle Path?
• Structural Realism.
• Entity Realism.
• Theories of Truth in Science.
• The No Miracles Argument.
• The Pessimistic Meta-induction Argument.
• Realism about Theories vs. Realism about Entities.
• The Ontic and Epistemic Views of Scientific Explanation.
• The Nature of Scientific Laws.
• Causation in Scientific Theories.
• Regularity Theories of Causal Relations.
• Counterfactual Theories of Causation.
• Probabilistic Causation.
• The Issue of Time in Causation.
• Laws Underlying Randomness and Chaos.
• Reductionism and Emergent Properties.
• Mechanisms in Scientific Explanation.
• The Role of Mathematics in Science.
• Ethics in Clinical Trials.
• Dual-use Dilemma in Scientific Research.
• Environmental Ethics and Science.
• Neuroethics: Implications of Neuroscience.
• Genetic Engineering and Moral Concerns.
• Science, Technology, and Society: Ethical Interactions.
• The Ethical Dimensions of Artificial Intelligence.
• Responsibility in Scientific Communication.
• Animal Experimentation: Ethical Pros and Cons.
• Data Privacy and Bioinformatics.
• The Social Construction of Scientific Knowledge.
• Science and Gender: Feminist Epistemology.
• Ethnoscience and Traditional Knowledge Systems.
• Public Understanding of Science.
• Science Communication and Media.
• Science Education and Cultural Context.
• Science Policy and Governance.
• The Role of Science in Democracy.
• Scientific Consensus and Controversies.
• The Relationship between Science, Industry, and Politics.
• Science and Religion: Conflict or Coexistence?
• Evolution vs. Creationism Debate.
• Cosmology, Big Bang, and Religious Interpretations.
• The Anthropic Principle and Design Arguments.
• Neurotheology: The Neural Basis of Religious Experiences.
• Miracles: A Philosophical Examination.
• The Concept of Soul in Science and Religion.
• Ethics: Secular vs. Religious Perspectives.
• Natural Theology and its Critiques.
• Non-Western Perspectives on Science and Spirituality.
• Interpretations of Quantum Mechanics.
• The Challenge of Dark Matter and Dark Energy.
• Complexity and Emergence in Science.
• Cognitive Science and the Nature of Consciousness.
• The Problem of Measurement in Science.
• Climate Science and Controversies.
• Evolutionary Psychology: Promises and Pitfalls.
• Neuroplasticity and the Changing Brain.
• The Limits of Computability.
• Theoretical Challenges in Modern Cosmology.
• Futuristic Perspectives on the Philosophy of Science.
• Posthumanism and the Future of Humanity.
• The Singularity: Myth or Inevitable Future?
• Ethics of Advanced AI and Superintelligent Machines.
• The Philosophy of Virtual Realities.
• Predictive Science and its Implications.
• Synthetic Biology and the Creation of Life.
• Space Exploration and the Search for Extraterrestrial Life.
• The Future of Medicine: Personalized, Predictive, and Preventive.
• The Post-Truth Era: Science in a World of Alternative Facts.
• Teleportation, Time Travel, and Other Scientific Frontiers.

The world of philosophy of science is vast, dynamic, and perpetually relevant, making the selection of philosophy of science research paper topics both an exciting and daunting task for students. These topics don’t just represent isolated academic inquiries; they influence and are influenced by the way we think, act, and perceive our world. It is the fusion of science, with its empirical rigor, and philosophy, with its reflective depth, that makes these topics an indispensable part of a student’s intellectual journey. As the next generation of philosophers of science, students have an unmatched opportunity to shape the discourse on the very nature and direction of scientific endeavor.

The Range of Philosophy of Science Research Paper Topics

Introduction

Science and philosophy are two stalwarts that have guided human understanding for centuries. While science seeks empirical explanations, philosophy delves into the conceptual foundations and implications of those explanations. The philosophy of science, then, acts as a bridge, linking these two domains and providing insights into the nature, methods, and values of scientific endeavors. For the budding philosopher or scientist, exploring the intertwined relationship between science and philosophy is not only enlightening but also pivotal for holistic academic growth.

Expansive Array of Topics Within Philosophy of Science

At first glance, the philosophy of science might seem like a narrow field. However, as one delves deeper, it becomes evident that the topics within this discipline are as varied as they are profound. From understanding the nature and structure of scientific theories to examining the ethical implications of scientific practices, the range is vast. Topics like scientific realism, causation, and demarcation between science and pseudoscience challenge students to question and reflect upon the fundamental aspects of scientific knowledge.

Historical Milestones in Scientific Philosophy

Tracing the history of the philosophy of science is akin to tracing the evolution of human thought. The ancients, from Aristotle to the medieval Islamic scholars, laid the groundwork for understanding the natural world. Their ideas, although sometimes flawed from a modern perspective, set the stage for the Scientific Revolution. Thinkers like Thomas Kuhn, with his concept of paradigm shifts, or Karl Popper, emphasizing falsifiability as a cornerstone of scientific validity, revolutionized how we think about scientific progress and knowledge. The transition from a time when science was indistinguishable from philosophy to an era of specialized scientific disciplines tells a tale of human achievement and the relentless pursuit of understanding.

Philosophy of Science: The Backbone of Scientific Advancements

At the heart of every significant scientific advancement, there lies a philosophical question. For instance, the shift from Newtonian mechanics to Einstein’s theory of relativity was not just a change in equations but a profound alteration in our understanding of space, time, and reality. Philosophical scrutiny is what differentiates science from mere observation. It raises questions like: “What counts as evidence?” “Are there limits to understanding?” “How do scientific models relate to reality?” Such reflections ensure that science remains grounded, self-critical, and progressive.

Another significant contribution of the philosophy of science is its emphasis on the ethical dimensions of scientific practices. As science advances, it often ventures into territories that were once the domain of speculative fiction: genetic engineering, artificial intelligence, and quantum computing, to name a few. Philosophers of science prompt us to ask not just “Can we?” but also “Should we?” By doing so, they ensure that scientific advancements benefit humanity and respect our shared values.

Significance of Selecting the Right Research Paper Topics

For a student of philosophy or science, selecting the right research paper topic is crucial. It’s not just about academic grades but about sparking a genuine interest and passion for the subject. Engaging with the right topic can lead to profound insights and even lay the foundation for future academic or research pursuits.

Diving deep into a topic like the ethical implications of AI, for instance, might lead one to explore the realms of cognitive science, machine learning, and moral philosophy. On the other hand, exploring the philosophical challenges posed by quantum mechanics might push students to grapple with the very nature of reality and causality.

In essence, the topic chosen becomes a window to the vast expanse of knowledge and inquiry, guiding the student’s academic trajectory. It’s no exaggeration to say that a well-chosen research topic in the philosophy of science can shape the intellectual growth of the individual, pushing boundaries and illuminating uncharted territories of thought.

The philosophy of science, with its myriad research paper topics, stands as a testament to humanity’s relentless pursuit of understanding. It’s not just an academic discipline but a reflection of our collective journey through time, charting our advancements, our questions, our challenges, and our aspirations. In today’s academic landscape, where interdisciplinarity and critical thinking are prized, the philosophy of science offers a rich, varied, and ever-evolving field of study. For those ready to embark on this intellectual voyage, the topics within this discipline promise not just academic excellence but a deeper understanding of the world and our place within it.

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• University of Pennsylvania
• School of Arts and Sciences
• Penn Calendar

University of Pennsylvania Philosophy

Philosophy of Science

Penn has considerable strength in philosophy of science and related areas of science studies. We are especially strong in the philosophy of the life and social sciences, the relations between the history of philosophy and the history of science, and the history of the philosophy of science.

Philosophy of the Natural Sciences and Mathematics

Our faculty work on diverse topics in the philosophy of natural sciences, but areas of special interest interest include philosophy of biology (Spencer, Weisberg), psychology and vision (Hatfield), learning theory (Bicchieri, Weinstein), the history of biology and psychology (Detlefsen, Hatfield), philosophy of chemistry (Weisberg), and Public Understanding of Science (Weisberg). A number of faculty also work directly in areas of the natural and formal sciences including cognitive science (Bicchieri, Hatfield, Weinstein, Weisberg), evolutionary and ecological modeling (Bicchieri, Weisberg), and computer science (Weinstein). Spencer, and Weisberg also work on many central topics of philosophy of science including explanation, the structure of theories, confirmation, and the social structure of science.

In addition, William Ewald (Law) teaches history and philosophy of mathematics, Steve Kimbrough (Wharton) teaches modeling, machine learning, and induction, Alan Kors and Ann Moyer (Department of History) offer courses in early modern intellectual history and history of science. History and Sociology of Science regularly offers courses in the history of biology (Lindee), the Scientific and Romantic revolutions (Kucuk, Tresch), and the history of technology (Voskuhl).

Philosophy of Social Science

Our faculty are also interested in some of the central questions in contemporary social science, such as: Are social beings with intentions producing collective outcomes nobody planned or predicted? Can groups of rational agents act in collectively beneficial ways? Can we explain features of the social world, like conventions and social norms, as the result of individuals’ beliefs and desires? How institutions evolve, and how can we model their dynamics?  

Cristina Bicchieri is interested in how norms may emerge and become stable, why an established norm may suddenly be abandoned, how is it possible that inefficient or unpopular norms survive, and what motivates people to obey norms. In order to answer some of these questions, she has combined evolutionary and game-theoretic tools with models of decision making drawn from cognitive and social psychology. For example, she has developed a theory of context-dependent preferences that explains the observed variability in norm compliance and is testing it in experimental games that involve pro-social norms of fairness and reciprocity.

The emergence of norms can be modeled in several ways, depending upon the type of norm that is investigated. Bicchieri and her students have studied how unpopular descriptive norms such as "bad" fashions and fads may occur as the result of negative informational cascades when agents are in the grip of 'pluralistic ignorance'. Often what we call a social norm is a stable behavioral disposition that is supported by a variety of strategies. Impersonal trust, for example, can evolve as a stable disposition in a population of conditionally "nice" agents. A surprising result of this evolutionary model is that what we take to be unconditional moral norms can only survive in populations of conditional choosers.

Michael Weisberg has developed agent-based models that explains how scientific communities coordinate in producing scientific results, and how the division of cognitive labor affects the success of the scientific enterprise. In particular, Weisberg has developed an agent-based computational model in which different research approaches (strategies) are distributed within an epistemic landscape, each approach having its own epistemic payoff. A natural question to ask is how much attention should scientists pay to what other scientists are doing, and what are the costs/benefits of doing so. Weisberg shows that the most effective communities (i.e., those with the highest payoff) are a combination of trendsetters (who open new research paths) and followers (who imitate the most successful members of the scientific community).

• Schools & departments

Philosophy of science

Working at the juncture between philosophy and research in the sciences, from physics to neuroscience and artificial intelligence

Philosophy of science is a thriving and interdisciplinary field of research, bringing together philosophy and cutting-edge science.

Key areas covered by researchers at Edinburgh include: metaphysical issues in science (philosophy of time and time-travel; realism and antirealism; natural kinds; causation; laws of nature); history and philosophy of science (especially, the history and philosophy of natural sciences); philosophy of psychology; philosophy of neuroscience; philosophy of cognitive sciences, foundations of probability, ethics of AI.

The group has strong links with the Higgs Centre for Theoretical Physics, the Royal Observatory Edinburgh, the Edinburgh Futures Institute, History at Edinburgh, the Institute for the Study of Science, Technology and Innovation, the Institute for Advanced Studies in the Humanities, and with the Science and Religion Programme in the School of Divinity.

• Edinburgh Futures Institute
• Higgs Centre for Theoretical Physics
• Institute for Advanced Studies in the Humanities
• Institute for the Study of Science Technology and Innovation
• Royal Observatory Edinburgh
• School of History, Classics & Archaeology
• Science and Religion Programme

Research questions

These are some of the research questions that faculty members in Philosophy of Science are interested in:

• What ethical implications do new technologies have?
• Can we trust AI?
• Is there a replication crisis in the human and social sciences?
• What is realism and antirealism about science?
• What is pluralism and perspectivism?
• Do natural kinds carve nature’s joints?
• What is causation?
• What is the nature of scientific explanation?
• Is fictionalism about science viable?
• What is the role of probability in scientific theories?
• What is Bayesianism?
• How to think of measurement in science?
• What is time? Is time-travel possible?
• What is a law of nature?
• What does it mean for scientific representations to be equivalent?
• What makes an attribute quantitative?

Core faculty working in this area include:

Other faculty and post-docs with interests in this area include:

• Michael Barany
• Nehal Bhuta
• Jane Calvert
• Luigi Del Debbio
• Miguel Garcia-Sancho
• Victoria Martin
• Alex Murphy
• John Peacock
• Pauline Phemister
• Wilson Poon
• Pablo Schyfter
• Steve Sturdy
• Nick Treanor
• Robin Williams

Postgraduate study

• MSc Philosophy  (specialisation in philosophy of science available)
• PhD and MSc by Research programmes

Meetings and events

Over the years we have been running a series of successful seminars entitled Contemporary Debates in Philosophy of Science . You can catch up with some of these seminars here:

• Nancy Cartwright
• Sabina Leonelli
• Margaret Morrison
• Samir Okasha
• Jacob Stegenga
• Alastair Wilson

We have also organised several workshops in collaboration with colleagues across the physical and the human sciences — please see here for a small sample of these events:

• Perspectival Realism
• Philosophy of the Natural and Human Sciences

Since 2014 we have run a MOOC called Philosophy and the Sciences , which is in two parts “Introduction to Philosophy of the Physical Sciences” and “Introduction to Philosophy of the Cognitive Sciences”.

• Introduction to the Philosophy of Physical Sciences
• Introduction to Philosophy of the Cognitive Sciences

Upcoming talks

Upcoming events are listed on the Philosophy events page

If you require a PDF document in an alternative format, such as large print or a coloured background, please contact the Undergraduate Teaching Office or email [email protected] (for Philosophy enquiries).

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• Introduction

Philosophy and natural science

Logical positivism and logical empiricism.

• Logics of discovery and justification
• Bayesian confirmation
• Eliminativism and falsification
• Underdetermination
• The work of Carl Hempel
• Difficulties
• Other approaches to explanation
• Scientific laws
• The axiomatic conception
• The semantic conception
• The historicist conception
• Unification and reduction
• The work of Thomas Kuhn
• Early arguments for realism
• The antirealism of van Fraassen, Laudan, and Fine
• “Piecemeal” realism
• Scientific truth
• Science as a social activity
• Feminist themes
• Progress and values

• Where did Auguste Comte go to school?
• What was Auguste Comte best known for?
• What did Werner Heisenberg do during World War II?
• What is Werner Heisenberg best known for?
• How did Werner Heisenberg contribute to atomic theory?

philosophy of science

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• Table Of Contents

philosophy of science , the study, from a philosophical perspective, of the elements of scientific inquiry. This article discusses metaphysical , epistemological, and ethical issues related to the practice and goals of modern science . For treatment of philosophical issues raised by the problems and concepts of specific sciences, see biology, philosophy of ; and physics, philosophy of .

From natural philosophy to theories of method

The history of philosophy is intertwined with the history of the natural sciences. Long before the 19th century, when the term science began to be used with its modern meaning , those who are now counted among the major figures in the history of Western philosophy were often equally famous for their contributions to “natural philosophy,” the bundle of inquiries now designated as sciences. Aristotle (384–322 bce ) was the first great biologist; René Descartes (1596–1650) formulated analytic geometry (“Cartesian geometry”) and discovered the laws of the reflection and refraction of light ; Gottfried Wilhelm Leibniz (1646–1716) laid claim to priority in the invention of the calculus ; and Immanuel Kant (1724–1804) offered the basis of a still-current hypothesis regarding the formation of the solar system (the Kant-Laplace nebular hypothesis ).

In reflecting on human knowledge, the great philosophers also offered accounts of the aims and methods of the sciences, ranging from Aristotle’s studies in logic through the proposals of Francis Bacon (1561–1626) and Descartes, which were instrumental in shaping 17th-century science. They were joined in these reflections by the most eminent natural scientists. Galileo (1564–1642) supplemented his arguments about the motions of earthly and heavenly bodies with claims about the roles of mathematics and experiment in discovering facts about nature. Similarly, the account given by Isaac Newton (1642–1727) of his system of the natural world is punctuated by a defense of his methods and an outline of a positive program for scientific inquiry. Antoine-Laurent Lavoisier (1743–94), James Clerk Maxwell (1831–79), Charles Darwin (1809–82), and Albert Einstein (1879–1955) all continued this tradition, offering their own insights into the character of the scientific enterprise.

Although it may sometimes be difficult to decide whether to classify an older figure as a philosopher or a scientist—and, indeed, the archaic “natural philosopher” may sometimes seem to provide a good compromise—since the early 20th century, philosophy of science has been more self-conscious about its proper role. Some philosophers continue to work on problems that are continuous with the natural sciences, exploring, for example, the character of space and time or the fundamental features of life . They contribute to the philosophy of the special sciences, a field with a long tradition of distinguished work in the philosophy of physics and with more-recent contributions in the philosophy of biology and the philosophy of psychology and neuroscience ( see mind, philosophy of ). General philosophy of science, by contrast, seeks to illuminate broad features of the sciences, continuing the inquiries begun in Aristotle’s discussions of logic and method. This is the topic of the present article.

A series of developments in early 20th-century philosophy made the general philosophy of science central to philosophy in the English-speaking world. Inspired by the articulation of mathematical logic, or formal logic , in the work of the philosophers Gottlob Frege (1848–1925) and Bertrand Russell (1872–1970) and the mathematician David Hilbert (1862–1943), a group of European philosophers known as the Vienna Circle attempted to diagnose the difference between the inconclusive debates that mark the history of philosophy and the firm accomplishments of the sciences they admired. They offered criteria of meaningfulness, or “cognitive significance,” aiming to demonstrate that traditional philosophical questions (and their proposed answers) are meaningless. The correct task of philosophy, they suggested, is to formulate a “logic of the sciences” that would be analogous to the logic of pure mathematics formulated by Frege, Russell, and Hilbert. In the light of logic, they thought, genuinely fruitful inquiries could be freed from the encumbrances of traditional philosophy.

To carry through this bold program, a sharp criterion of meaningfulness was required. Unfortunately, as they tried to use the tools of mathematical logic to specify the criterion, the logical positivists (as they came to be known) encountered unexpected difficulties. Again and again, promising proposals were either so lax that they allowed the cloudiest pronouncements of traditional metaphysics to count as meaningful, or so restrictive that they excluded the most cherished hypotheses of the sciences ( see verifiability principle ). Faced with these discouraging results, logical positivism evolved into a more moderate movement, logical empiricism. (Many historians of philosophy treat this movement as a late version of logical positivism and accordingly do not refer to it by any distinct name.) Logical empiricists took as central the task of understanding the distinctive virtues of the natural sciences. In effect, they proposed that the search for a theory of scientific method — undertaken by Aristotle, Bacon, Descartes, and others—could be carried out more thoroughly with the tools of mathematical logic. Not only did they see a theory of scientific method as central to philosophy, but they also viewed that theory as valuable for aspiring areas of inquiry in which an explicit understanding of method might resolve debates and clear away confusions. Their agenda was deeply influential in subsequent philosophy of science.

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Scientific Method

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

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

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

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

1. Overview and organizing themes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3. Logic of method and critical responses

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5. Method in Practice

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

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

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

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

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

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

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

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

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

5.2 Computer methods and ‘new ways’ of doing science

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

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

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

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

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

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

6. Discourse on scientific method

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Understanding Science

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• The philosophy of science is a field that deals with what science is, how it works, and the logic through which we build scientific knowledge.
• In this website, we present a rough synthesis of some new and some old ideas from the philosophy of science.

The philosophy of science

In this website, we use a practical checklist to get a basic picture of what ​​ science  is and a flexible flowchart to depict how science works. For most everyday purposes, this gives us a fairly complete picture of what science is and is not. However, there is an entire field of rigorous academic study that deals specifically with what science is, how it works, and the logic through which we build scientific knowledge. This branch of philosophy is handily called the philosophy of science. Many of the ideas that we present in this website are a rough synthesis of some new and some old ideas from the philosophy of science.

Despite its straightforward name, the field is complex and remains an area of current inquiry. Philosophers of science actively study such questions as:

• What is a ​​ law  of nature? Are there any in non-physical sciences like biology and psychology?
• What kind of ​​ data  can be used to distinguish between real causes and accidental regularities?
• How much ​​ evidence  and what kinds of evidence do we need before we accept ​​ hypotheses ?
• Why do scientists continue to rely on ​​ models  and ​​ theories  which they know are at least partially inaccurate (like Newton’s physics)?

Though they might seem elementary, these questions are actually quite difficult to answer satisfactorily. Opinions on such issues vary widely within the field (and occasionally part ways with the views of scientists themselves — who mainly spend their time  doing  science, not analyzing it abstractly). Despite this diversity of opinion, philosophers of science can largely agree on one thing: there is no single, simple way to define science!

Though the field is highly specialized, a few touchstone ideas have made their way into the mainstream. Here’s a quick explanation of just a few concepts associated with the philosophy of science, which you might (or might not) have encountered.

• Epistemology  — branch of philosophy that deals with what knowledge is, how we come to ​​ accept  some things as true, and how we justify that acceptance.
• Empiricism  — set of philosophical approaches to building knowledge that emphasizes the importance of ​​ observable  evidence from the ​​ natural world .
• Induction  — method of reasoning in which a generalization is argued to be true based on individual examples that seem to fit with that generalization. For example, after observing that trees, bacteria, sea anemones, fruit flies, and humans have cells, one might  inductively  ​​ infer  that all organisms have cells.
• Deduction  — method of reasoning in which a conclusion is logically reached from premises. For example, if we know the current relative positions of the moon, sun, and Earth, as well as exactly how these move with respect to one another, we can ​​ deduce  the date and location of the next solar eclipse.
• Parsimony/Occam’s razor  — idea that, all other things being equal, we should prefer a simpler explanation over a more complex one.
• Demarcation problem  — the problem of reliably distinguishing science from non-science. Modern philosophers of science largely agree that there is no single, simple criterion that can be used to demarcate the boundaries of science.
• Falsification  — the view, associated with philosopher Karl Popper, that evidence can only be used to rule out ideas, not to support them. Popper proposed that scientific ideas can only be ​​ tested  through ​​ falsification , never through a search for supporting evidence.
• Paradigm shifts and scientific revolutions  — a view of science, associated with philosopher Thomas Kuhn, which suggests that the history of science can be divided up into times of normal science (when scientists add to, elaborate on, and work with a central, accepted scientific theory) and briefer periods of revolutionary science. Kuhn asserted that during times of revolutionary science, anomalies refuting the accepted theory have built up to such a point that the old theory is broken down and a new one is built to take its place in a so-called “paradigm shift.”

Who’s who in the philosophy of science

If you’re interested in learning more about the philosophy of science, you might want to begin your investigation with some of the big names in the field:

Aristotle (384-322 BC) — Arguably the founder of both science and philosophy of science. He wrote extensively about the topics we now call physics, astronomy, psychology, biology, and chemistry, as well as logic, mathematics, and epistemology.

Francis Bacon (1561-1626) — Promoted a scientific method in which scientists gather many ​​ facts  from observations and ​​ experiments , and then make ​​ inductive inferences  about patterns in nature.

Rene Descartes (1596-1650) — Mathematician, scientist, and philosopher who promoted a scientific method that emphasized deduction from first principles. These ideas, as well as his mathematics, influenced Newton and other figures of the Scientific Revolution.

Piere Duhem (1861-1916) — Physicist and philosopher who defended an extreme form of empiricism. He argued that we cannot draw conclusions about the existence of unobservable entities conjectured by our theories such as atoms and molecules.

Carl Hempel (1905-1997) — Developed influential theories of scientific explanation and theory confirmation. He argued that a phenomenon is “explained” when we can see that it is the logical consequence of a law of nature. He championed a hypothetico-deductive account of confirmation, similar to the way we characterize “making a ​​ scientific argument ” in this website.

Karl Popper (1924-1994) — Argued that falsifiability is both the hallmark of scientific theories and the proper methodology for scientists to employ. He believed that scientists should always regard their theories with a skeptical eye, seeking every opportunity to try to falsify them.

Thomas Kuhn (1922-1996) — Historian and philosopher who argued that the picture of science developed by logical empiricists such as Popper didn’t resemble the history of science. Kuhn famously distinguished between normal science, where scientists solve puzzles within a particular framework or paradigm, and revolutionary science, when the paradigm gets overturned.

Paul Feyerabend (1924-1994) — A rebel within the philosophy of science. He argued that there is no scientific method or, in his words, “anything goes.” Without regard to rational guidelines, scientists do whatever they need to in order to come up with new ideas and persuade others to accept them.

Evelyn Fox Keller (1936-) — Physicist, historian, and one of the pioneers of feminist philosophy of science, exemplified in her study of Barbara McClintock and the history of genetics in the 20th century.

Elliott Sober (1948-) — Known for his work on ​​ parsimony  and the conceptual foundations of evolutionary biology. He is also an important contributor to the biological theory of group selection.

Nancy Cartwright (1944-) — Philosopher of physics known for her claim that the laws of physics “lie” — i.e., that the laws of physics only apply in highly idealized circumstances. She has also worked on causation, interpretations of probability and quantum mechanics, and the metaphysical foundations of modern science.

• Take a sidetrip

Learn about specialized topics in the philosophy of science with the  Stanford Encyclopedia of Philosophy .

Source material: Godfrey-Smith, P. 2003. Theory and Reality. Chicago: The University of Chicago Press.

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The Philosophy of Science: An Overview

• First Online: 18 May 2022

Cite this chapter

• Amelia Kehoe 4 ,
• Charlotte Rothwell 5 &
• Robyn Bluhm 6  

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There has been a recent move within health professions education towards greater methodological rigor. This move springs, at least in part, from the increasing popularity of philosophy of science as a methodological concern; we are seeing now, more than ever, how philosophy can have an important and productive impact on science. Particularly within health professions education, which many have argued should be considered a social science, there is a sense that researchers must engage with the questions of philosophy. However, this has not always been the case; the methods of the natural sciences once being seen as the model for all science. This view is based on a lack of knowledge of philosophy of science. In this chapter, we explore the definition and breadth of this field, whilst emphasising the practical application of philosophy of science terms such as ‘paradigm’, ‘ontology’ and ‘epistemology’ for those conducting and interpreting health professions education research. Given that philosophy of science theories and concepts underpin inquiry and practice within many scientific disciplines, we aim to set the scene in this chapter in regard to the context in which health professions education pedagogy and research exist.

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Amelia Kehoe

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Kehoe, A., Rothwell, C., Bluhm, R. (2022). The Philosophy of Science: An Overview. In: Brown, M.E.L., Veen, M., Finn, G.M. (eds) Applied Philosophy for Health Professions Education. Springer, Singapore. https://doi.org/10.1007/978-981-19-1512-3_13

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Current research interests/topics in philosophy of science.

Just kind of curious. Reading/listening around I get the impression that philosophy has its own 'paradigm shifts' from logical positivism (Vienna Circle, Popper, etc) to sociological/historical study (Kuhn, Lakatos, etc.). But obviously a lot can change in a couple decades and the books I was reading don't really cover past that - so I'm wondering, is there a new paradigm of philosophy of science? What are the 'hot topics' for research nowadays?

I'm tempted to say something to do with feminist/social epistemology or discussing the roles of 'non-scientific' values in science? But I don't really have much knowledge on anything recent, that's just something that seemed new compared to what I'd read about the history of it.

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Multiple goals, multiple solutions, plenty of second-guessing and revising − here’s how science really works

Professor of Philosophy, University of Montana

Disclosure statement

Soazig Le Bihan receives funding from the Maureen and Mike Mansfield Center at the University of Montana.

University of Montana provides funding as a member of The Conversation US.

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A man in a lab coat bends under a dim light, his strained eyes riveted onto a microscope. He’s powered only by caffeine and anticipation.

This solitary scientist will stay on task until he unveils the truth about the cause of the dangerous disease quickly spreading through his vulnerable city. Time is short, the stakes are high, and only he can save everyone. …

That kind of romanticized picture of science was standard for a long time. But it’s as far from actual scientific practice as a movie’s choreographed martial arts battle is from a real fistfight.

For most of the 20th century, philosophers of science like me maintained somewhat idealistic claims about what good science looks like. Over the past few decades, however, many of us have revised our views to better mirror actual scientific practice .

An update on what to expect from actual science is overdue. I often worry that when the public holds science to unrealistic standards, any scientific claim failing to live up to them arouses suspicion. While public trust is globally strong and has been for decades, it has been eroding. In November 2023, Americans’ trust in scientists was 14 points lower than it had been just prior to the COVID-19 pandemic, with its flurry of confusing and sometimes contradictory science-related messages.

When people’s expectations are not met about how science works, they may blame scientists. But modifying our expectations might be more useful. Here are three updates I think can help people better understand how science actually works. Hopefully, a better understanding of actual scientific practice will also shore up people’s trust in the process.

The many faces of scientific research

First, science is a complex endeavor involving multiple goals and associated activities.

Some scientists search for the causes underlying some observable effect, such as a decimated pine forest or the Earth’s global surface temperature increase .

Others may investigate the what rather than the why of things. For example, ecologists build models to estimate gray wolf abundance in Montana . Spotting predators is incredibly challenging. Counting all of them is impractical. Abundance models are neither complete nor 100% accurate – they offer estimates deemed good enough to set harvesting quotas. Perfect scientific models are just not in the cards .

Beyond the what and the why, scientists may focus on the how. For instance, the lives of people living with chronic illnesses can be improved by research on strategies for managing disease – to mitigate symptoms and improve function, even if the true causes of their disorders largely elude current medicine.

It’s understandable that some patients may grow frustrated or distrustful of medical providers unable to give clear answers about what causes their ailment. But it’s important to grasp that lots of scientific research focuses on how to effectively intervene in the world to reach some specific goals.

Simplistic views represent science as solely focused on providing causal explanations for the various phenomena we observe in this world. The truth is that scientists tackle all kinds of problems, which are best solved using different strategies and approaches and only sometimes involve full-fledged explanations.

Complex problems call for complex solutions

The second aspect of scientific practice worth underscoring is that, because scientists tackle complex problems, they don’t typically offer one unique, complete and perfect answer. Instead they consider multiple, partial and possibly conflicting solutions.

Scientific modeling strategies illustrate this point well. Scientific models typically are partial, simplified and sometimes deliberately unrealistic representations of a system of interest. Models can be physical, conceptual or mathematical. The critical point is that they represent target systems in ways that are useful in particular contexts of inquiry. Interestingly, considering multiple possible models is often the best strategy to tackle complex problems.

Scientists consider multiple models of biodiversity , atomic nuclei or climate change . Returning to wolf abundance estimates, multiple models can also fit the bill. Such models rely on various types of data, including acoustic surveys of wolf howls, genetic methods that use fecal samples from wolves, wolf sightings and photographic evidence, aerial surveys, snow track surveys and more.

Weighing the pros and cons of various possible solutions to the problem of interest is part and parcel of the scientific process. Interestingly, in some cases, using multiple conflicting models allows for better predictions than trying to unify all the models into one.

The public may be surprised and possibly suspicious when scientists push forward multiple models that rely on conflicting assumptions and make different predictions. People often think “real science” should provide definite, complete and foolproof answers to their questions. But given various limitations and the world’s complexity, keeping multiple perspectives in play is most often the best way for scientists to reach their goals and solve the problems at hand.

Science as a collective, contrarian endeavor

Finally, science is a collective endeavor, where healthy disagreement is a feature, not a bug.

The romanticized version of science pictures scientists working in isolation and establishing absolute truths. Instead, science is a social and contrarian process in which the community’s scrutiny ensures we have the best available knowledge. “Best available” does not mean “definitive,” but the best we have until we find out how to improve it. Science almost always allows for disagreements among experts.

Controversies are core to how science works at its best and are as old as Western science itself. In the 1600s, Descartes and Leibniz fought over how to best characterize the laws of dynamics and the nature of motion.

The long history of atomism provides a valuable perspective on how science is an intricate and winding process rather than a fast-delivery system of results set in stone. As Jean Baptiste Perrin conducted his 1908 experiments that seemingly settled all discussion regarding the existence of atoms and molecules, the questions of the atom’s properties were about to become the topic of decades of controversies with the birth of quantum physics.

The nature and structure of fundamental particles and associated fields have been the subject of scientific research for more than a century. Lively academic discussions abound concerning the difficult interpretation of quantum mechanics , the challenging unification of quantum physics and relativity , and the existence of the Higgs boson , among others.

Distrusting researchers for having healthy scientific disagreements is largely misguided.

A very human practice

To be clear, science is dysfunctional in some respects and contexts. Current institutions have incentives for counterproductive practices, including maximizing publication numbers . Like any human endeavor, science includes people with bad intent, including some trying to discredit legitimate scientific research . Finally, science is sometimes inappropriately influenced by various values in problematic ways.

These are all important considerations when evaluating the trustworthiness of particular scientific claims and recommendations. However, it is unfair, sometimes dangerous, to mistrust science for doing what it does at its best. Science is a multifaceted endeavor focused on solving complex problems that typically just don’t have simple solutions. Communities of experts scrutinize those solutions in hopes of providing the best available approach to tackling the problems of interest.

Science is also a fallible and collective process. Ignoring the realities of that process and holding science up to unrealistic standards may result in the public calling science out and losing trust in its reliability for the wrong reasons.

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