Philosophy of science meets statistical inference: reviving the concept of corroboration
The most common way of testing statistical hypotheses is to conduct null hypothesis significance tests (NHST) and to use a p-value to describe evidence against the null hypothesis. In this talk, I would like to highlight a fundamental conceptual problem with this approach: the impossibility to express support for the null hypothesis. Since null hypotheses are often simple and precise idealizations of complex models with substantial theoretical importance, a good method for scientific hypothesis tests has to be able to express support for them. Also, the one-sided nature replication of crisis p-values in various arguably scientific aggravates the disciplines. My approach is twofold: first I explain why classical NHST and classical Bayesian inference fail to evaluate a null hypothesis in an appropriate way; then I develop a measure of corroboration, taking inspiration from both Bayesian and frequentist procedures. I argue that degrees of corroboration achieve a more nuanced judgment on the evidence in favor of a null hypothesis and that they can be used in a variety of cases in statistical inference.
Friday, 22 June 2018, ore 14:30 — Sala Wataghin