Hypothesis testing and confidence sets: why Bayesian not frequentist, and how to set a prior with a regulatory authority

TL;DR

This paper advocates for Bayesian hypothesis testing and confidence sets over frequentist methods, emphasizing their admissibility, information efficiency, and practical advantages in real-world inference problems, especially with prior agreement.

Contribution

It provides a comprehensive argument for Bayesian methods' superiority, introduces criteria for admissibility, and demonstrates practical benefits through examples and prior-setting strategies.

Findings

Bayesian solutions are admissible and satisfy common-sense criteria.

Bayesian methods require significantly less data than frequentist approaches in some cases.

Bayesian priors enable construction of confidence regions that retain maximum information.

Abstract

We marshall the arguments for preferring Bayesian hypothesis testing and confidence sets to frequentist ones. We define admissible solutions to inference problems, noting that Bayesian solutions are admissible. We give seven weaker common-sense criteria for solutions to inference problems, all failed by these frequentist methods but satisfied by any admissible method. We note that pseudo-Bayesian methods made by handicapping Bayesian methods to satisfy criteria on type I error rate makes them frequentist not Bayesian in nature. We give five examples showing the differences between Bayesian and frequentist methods; the first requiring little calculus, the second showing in abstract what is wrong with these frequentist methods, the third to illustrate information conservation, the fourth to show that the same problems arise in everyday statistical problems, and the fifth to illustrate how on some real-life inference problems Bayesian methods require less data than fixed sample-size (resp. pseudo-Bayesian) frequentist hypothesis testing by factors exceeding 3000 (resp 300) without recourse to informative priors. To address the issue of different parties with opposing interests reaching agreement on a prior, we illustrate the beneficial effects of a Bayesian "Let the data decide" policy both on results under a wide variety of conditions and on motivation to reach a common prior by consent. We show that in general the frequentist confidence level contains less relevant Shannon information than the Bayesian posterior, and give an example where no deterministic frequentist critical regions give any relevant information even though the Bayesian posterior contains up to the maximum possible amount. In contrast use of the Bayesian prior allows construction of non-deterministic critical regions for which the Bayesian posterior can be recovered from the frequentist confidence.