Abstract

According to influential accounts of scientific method, e.g., critical rationalism, scientific knowledge grows by repeatedly testing our best hypotheses. In comparison to rivaling accounts of scientific reasoning such as Bayesianism, these accounts are closer to crucial aspects of scientific practice. But despite the preeminence of hypothesis tests in statistical inference, their philosophical foundations are shaky. In particular, the interpretation of "insignificant results"---outcomes where the tested hypothesis has survived the test---poses a major epistemic challenge that is not sufficiently addressed by the standard methodology for conducting such tests. In this paper, I argue that a quantitative explication of degree of corroboration can fill this important methodological and epistemological gap. First, I argue that this concept is distinct from the Bayesian notion of evidential support and that it plays an independent role in scientific reasoning. Second, I demonstrate that degree of corroboration cannot be suitably explicated in a probabilistic relevance framework, as proposed by Popper. Third, I derive two measures of corroboration that possess a large number of attractive properties, establish an insightful relation between corroboration and evidential support and are not committed to a Bayesian or a frequentist framework. In sum, the paper rethinks the foundations of inductive inference by providing a novel logic of hypothesis testing.