Beyond Neyman-Pearson: E-values enable hypothesis testing with a data-driven alpha.

ABSTRACT: A standard practice in statistical hypothesis testing is to mention the P-value alongside the accept/reject decision. We show the advantages of mentioning an e-value instead. With P-values, it is not clear how to use an extreme observation (e.g. [Formula: see text]) for getting better frequentist decisions. With e-values it is straightforward, since they provide Type-I risk control in a generalized Neyman-Pearson setting with the decision task (a general loss function) determined post hoc, after observation of the data-thereby providing a handle on "roving [Formula: see text]'s." When Type-II risks are taken into consideration, the only admissible decision rules in the post hoc setting turn out to be e-value-based. Similarly, if the loss incurred when specifying a faulty conf

SUBMITTER: Grunwald PD 

PROVIDER: S-EPMC11441548 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature