For more than three decades, Deborah Mayo has defended severe testing, a framework for evaluating the strength of scientific evidence that provides philosophical foundations for (variants of) classical statistical methods. In her newest book, Statistical Inference as Severe Testing, Mayo (1) extends and refines her views on severe testing, (2) takes aim at researchers who claim that the reproducibility crisis is a result of the use of classical statistics, and (3) exposes some contemporary Bayesian methods as lacking philosophical foundations. The book is engaging, sometimes funny, and often insightful. After reviewing the basics of severe testing, I summarize three lessons from Mayo's book that are valuable for philosophers and scientists alike. I then discuss one way in which I think Mayo's arguments could be tightened.

Mayo argues that one has evidence for a hypothesis to the extent the hypothesis has passed a...

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