Speaker: Peter Grünwald, CWI
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. We present the E-value, a recently popularized notion of evidence which overcomes some of these issues. While E-values have lain dormant until 2019, their use has recenty exploded - we just had a one-week workshop (‘SAVI’ - safe anytime-valid inference) with attendees from netflix, booking (A/B testing), clinical trial design and meta-analysis. In simple cases, the E-value coincides with the Bayes factor, the notion of evidence preferred by Bayesians. But if the null is composite or nonparametric, or an alternative cannot be explicitly formulated, E-values and Bayes factors become distinct. Unlike the Bayes factor, E-values allow for tests with strict ‘ classical’ Type-I error control under optional continuation and combination of data from different sources. They are also the basic building blocks of anytime-valid confidence intervals that remain valid under optional stopping.
Note: this meeting was performed in person and was not recorded.