.png){kind=icon}
.png){kind=icon}
Return
Beyond Neyman-Pearson: E-values enable hypothesis testing with a data-driven alpha
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.2302098121.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. P << a ) 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 alpha '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 confidence interval is not fixed in advance, standard confidence intervals and distributions may fail, whereas e-confidence sets and e-posteriors still provide valid risk guarantees. Sufficiently powerful e-values have by now been developed for a range of classical testing problems. We discuss the main challenges for wider development and deployment.
Keywords:
hypothesis testing
decision theory
-values
confidence
evidence
Journal
P
IF:
9.1
Papers:
10.8W
Citations:
73.5W