Philosophy of science is the branch of philosophy concerned with the foundations, methods, aims, and implications of scientific inquiry. It asks how evidence supports theories, what distinguishes scientific explanation from description, whether successful theories reveal reality, and how scientific knowledge changes. It overlaps with epistemology, which studies knowledge and justification, and metaphysics, which investigates the structure of reality. Rather than prescribing a single research procedure, it examines both the reasoning and the practices through which sciences establish and revise their claims. (plato.stanford.edu)
Historical development
Reflection on scientific knowledge predates the modern separation of science from philosophy. Aristotle examined demonstration, experience, and explanatory principles. During the development of modern science, philosophers debated the roles of observation, mathematical reasoning, and experiment. Empiricism emphasized experience as a source of knowledge, while disagreements persisted about whether experience alone could establish general principles. These questions supplied enduring problems for the philosophy of scientific method. (plato.stanford.edu)
In the twentieth century, logical empiricism brought scientific language, confirmation, and logical analysis into the foreground. Thinkers including Rudolf Carnap sought to clarify the relationship between theoretical claims and observational evidence. The movement was internally diverse: its members did not share one fixed verification criterion or a uniform rejection of every form of metaphysical inquiry. Subsequent approaches increasingly incorporated scientific history, experimental practice, and the organization of research communities. (plato.stanford.edu)
Evidence, induction, and testing
A central question is how limited observations justify claims extending beyond what has been observed. Inductive reasoning supports generalizations and predictions without guaranteeing their truth. David Hume formulated the influential problem of induction: appealing to past regularities to justify expectations about unobserved cases appears to presuppose the reliability of the very inference requiring justification. This differs from deductive reasoning, in which true premises and a valid argument guarantee the conclusion. (plato.stanford.edu)
Bayesian inference provides a framework for representing how evidence changes degrees of confidence in a hypothesis. Through Bayes’ theorem, prior probabilities are updated using the probability of the evidence under competing hypotheses. Philosophical debates concern the justification of initial probabilities and whether probabilistic updating resolves, reformulates, or leaves untouched the deeper problem of induction. Evidence may increase a hypothesis’s credibility without establishing its truth conclusively. (plato.stanford.edu)
Karl Popper emphasized falsifiability rather than verification: scientific theories should expose themselves to possible empirical refutation. Surviving demanding tests constitutes corroboration, not proof. Testing is nevertheless complicated because predictions usually depend on auxiliary assumptions about instruments, initial conditions, and other theories. A failed prediction therefore does not automatically identify which assumption is responsible. (plato.stanford.edu)
Explanation, models, and causation
Scientific explanation concerns why phenomena occur, rather than merely recording or predicting them. Carl Hempel’s deductive-nomological model characterized explanation as deriving an event from general laws and relevant conditions. Objections showed that a formally valid derivation can lack explanatory relevance or fail to capture the direction of explanation. Later accounts emphasized statistical relevance, causal processes, mechanisms, or the unification of apparently different phenomena. (plato.stanford.edu)
Questions about causation are especially important where researchers distinguish causal relationships from statistical associations. Models also raise questions about idealization: a representation may omit details or introduce simplifying assumptions while still supporting understanding. Philosophers consequently distinguish predictive accuracy, explanatory relevance, and faithful representation; success in one respect need not establish success in all three. (plato.stanford.edu)
Realism and scientific change
Scientific realism holds, broadly, that successful scientific theories can provide approximately true knowledge of a mind-independent world, including its unobservable features. A prominent argument is that the predictive success of science would otherwise be difficult to explain. Critics point to historically successful theories later rejected and to the possibility that different theories accommodate the same evidence. Antirealist approaches may accept a theory’s empirical adequacy without endorsing its full account of unobservable reality. (plato.stanford.edu)
Thomas Kuhn offered an influential account of scientific change in The Structure of Scientific Revolutions (1962). He distinguished normal science, organized around shared exemplary achievements and commitments, from revolutionary periods in which those commitments change. His concept of a paradigm connected theory with research problems, standards, and practices. Incommensurability describes difficulties in comparing frameworks whose concepts or evaluative standards differ; it should not simply be equated with the impossibility of rational comparison. (plato.stanford.edu)
Objectivity and research communities
Scientific objectivity can mean fidelity to facts, freedom from individual bias, or reliability achieved through public criticism. Philosophers examine how background theories shape observation and how social organization affects the assessment of evidence. Some accounts locate objectivity less in an entirely perspective-free individual than in procedures enabling researchers to identify and challenge one another’s assumptions. (plato.stanford.edu)
This brings philosophy of science into contact with social epistemology. Reproducibility, transparent methods, and critical scrutiny are examined as potential safeguards, not guarantees of correctness. Debates also distinguish epistemic values, such as consistency and explanatory scope, from social values influencing research priorities or judgments about acceptable uncertainty. The issue is not merely whether values enter science, but which roles they can legitimately play in producing and assessing knowledge. (plato.stanford.edu)