Falsifiability is a deductive standard of evaluation of scientific theories and hypotheses, introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery (1934).[upper-alpha 2] A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test.
Popper proposed falsifiability as the cornerstone solution to both the problem of induction and the problem of demarcation. He insisted that, as a logical criterion, falsifiability is distinct from the related concept "capacity to be proven wrong" discussed in Lakatos' falsificationism.[upper-alpha 3][upper-alpha 4][upper-alpha 5] Even being a logical criterion, its purpose is to make the theory predictive and testable, and thus useful in practice.
Popper contrasted falsifiability to the intuitively similar concept of verifiability that was then current in logical positivism. His argument goes that the only way to verify a claim such as "All swans are white" would be if one could theoretically observe all swans,[upper-alpha 6] which is not possible. Instead, falsifiability searches for the anomalous instance, such that observing a single black swan is theoretically reasonable and sufficient to logically falsify the claim. On the other hand, the Duhem–Quine thesis says that definitive experimental falsifications are impossible and that no scientific hypothesis is by itself capable of making predictions, because an empirical test of the hypothesis requires one or more background assumptions.
According to Popper there is a clean asymmetry on the logical side[upper-alpha 7] and falsifiability does not have the Duhem problem[upper-alpha 8] because it is a logical criterion. Experimental research has the Duhem problem and other problems, such as induction,[upper-alpha 9] but, according to Popper, statistical tests, which are only possible when a theory is falsifiable, can still be useful within a critical discussion. Philosophers such as Deborah Mayo consider that Popper "comes up short" in his description of the scientific role of statistical and data models.
As a key notion in the separation of science from non-science and pseudoscience, falsifiability has featured prominently in many scientific controversies and applications, even being used as legal precedent.