AI 中文总结
研究指出科学家因认识论框架不同在证据解释上有分歧，证实主义和证伪主义各有局限。贝叶斯认识论兼顾二者优点并弥补弱点，证实和证伪是其特例，对研究设计等有实际影响，采用它可减少研究者摩擦，提升研究效率。
AI 中文摘要
科学家常常在如何解释证据而非数据上存在分歧，因为他们在不同的认识论框架下隐性操作却未意识到。证实主义和证伪主义这两种主流传统虽各有见解，但都有局限。贝叶斯认识论提供了解决方案，它以概率处理证据，基于有限可修正的假设集，兼顾了两种传统的优点并弥补弱点。还表明证实和证伪是贝叶斯更新的特例，对先验主观性的反对没那么强，且对研究设计等有实际影响，采用它能减少研究者间摩擦等。
英文摘要
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.
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