 Deborah Mayo has argued (particularly in Mayo 1991, 1996, and 2014) that the intuition that predictivism is true derives from a premium on severe tests of hypotheses. A test of a hypothesis H is severe to the extent that H is unlikely to pass that test if H is false. Intuitively, if a novel consequence N is shown to follow from H, and the probability of N on the assumption \({\sim}H\) is very low (for the reason of its being novel), then testing for N would seem to count as a severe test of H, and a positive outcome should strongly support H. Here novelty and severity appear to coincide—but Mayo observes that there are cases in which they come apart. For example, it has seemed to many that if H is built to fit some body of evidence E then the fact that H fits E does not support H because this fit does not constitute H’s having survived a severe test (or a test at all). One of Mayo’s central objectives is to expose the fallacies that this latter reasoning involves.