Scientists routinely disagree not about data but about how to interpret evidence, because they implicitly operate from different epistemological frameworks without recognising it. The two dominant traditions, confirmationism and falsificationism, each capture genuine insights about scientific reasoning but face well-documented limitations. Confirmationism provides a natural account of how evidence supports hypotheses but cannot escape the problem of induction. Falsificationism provides logical rigour through deductive refutation but is undermined by the Duhem-Quine problem and offers no account of how scientists rationally accept theories and act on them. Here we argue that Bayesian epistemology provides a practical resolution to this impasse. By treating evidence probabilistically and operating over a finite, revisable set of hypotheses, the framework recovers the valid contributions of both traditions while addressing their core weaknesses. We show that confirmation and falsification emerge as special cases of Bayesian updating, that the subjectivity objection to priors is weaker than commonly supposed, and that the framework has direct practical consequences for study design, evidence synthesis, and publishing norms. Specifically, it replaces the falsifiability criterion with the more useful question of whether a hypothesis makes predictions that discriminate between competitors, and reframes the reproducibility crisis as an epistemological rather than a purely statistical problem. Adopting Bayesian epistemology, even informally as a mental model, can reduce friction between researchers, improve research efficiency, and help restore the cumulative character of scientific progress.

翻译：暂无翻译