Fiducialize statistical significance: transforming p-values into conservative posterior probabilities and Bayes factors

Abstract

One remedy to the misuse of p-values transforms them to bounds on Bayes factors. With a prior probability of the null hypothesis, such a bound gives a lower bound on the posterior probability. Unfortunately, knowing a posterior probability is above some number cannot ensure that the null hypothesis is improbable enough to warrant its rejection. For example, if the lower bound is 0.0001, that implies that the posterior probability is at least 0.0001 but does not imply it is lower than 0.05 or even 0.9. A fiducial argument suggests an alternative estimate of the posterior probability that the null hypothesis is true. In the case that the prior probability of the null hypothesis is 50%, the estimated posterior probability is about $\text{p}{(\ln \text{p})}^2$ for low p. In other cases, each occurrence of $\text{p}$ in the formula is the p-value calibrated by multiplying it by the prior odds of the null hypothesis. In the absence of a prior, $\text{p}{(\ln \text{p})}^2$ also serves as an asymptotic Bayes factor. Since the fiducial estimate of the posterior probability is greater than the lower bounds, its use in place of a bound leads to more stringent hypothesis testing. Making that replacement in a rationale for 0.005 as the significance level reduces the level to 0.001.

Keywords:

• Bayes factor
• fiducial hypothesis testing
• fiducial model averaging
• local false discovery rate
• replication crisis
• reproducibility crisis

2020 Mathematics Subject Classifications:

• 62A01
• 62P25
• 62P15

Acknowledgments

I thank the anonymous reviewers for their feedback, especially for one reviewer's insightful comments, which led to the addition of Section 3 and to improved clarity throughout.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Nearly all of this research, including the statement and proof of Theorem 2.1, was performed at the University of Ottawa with partial support from the Natural Sciences and Engineering Research Council of Canada (RGPIN/356018-2009).