Title: E is the New P

Abstract: How much evidence do the data give us about one hypothesis versus another? The standard way to measure evidence is still the p-value,  despite a myriad of problems surrounding it. In this talk I will provide a gentle introduction to the e-value (wikipedia), a  recently popularized notion of  evidence which overcomes some of these  issues. E-values, which have a very concrete interpretation in terms of betting strategies, were only given a name as recently as 2019. Since then, interest in them has exploded with dedicated workshops and 100s of papers, both theoretical and applied, many in top journals such as the Annals of Statistics. Crucially, e-values allow for effortless testing under optional continuation of data collection and combination of data from different sources – something that practitioners yearn for, yet  is simply impossible to do with the classical approaches. Relatedly, they resolve some paradoxes involving p-values and counterfactuals and the likelihood principle. We will discuss these facets and end by a comparison with Bayesian methods.

Main literature:

G., De Heide, Koolen. Safe Testing. Journal of the Royal Statistical Society Series B, 2024 (first version appeared on arXiv 2019).

G.  Beyond Neyman-Pearson: e-values enable hypothesis testing with a data-driven alpha. Proceedings National Academy of Sciences of the USA (PNAS), 2024.

The seminar will held online on March 12th at 14:30 (Rome time) on the Microsoft Teams platform, here.