Published online by Cambridge University Press: 01 April 2022
The likelihood principle of Bayesian statistics implies that information about the stopping rule used to collect evidence does not enter into the statistical analysis. This consequence confers an apparent advantage on Bayesian statistics over frequentist statistics. In the present paper, I argue that information about the stopping rule is nevertheless of value for an assessment of the reliability of the experiment, which is a pre-experimental measure of how well a contemplated procedure is expected to discriminate between hypotheses. I show that, when reliability assessments enter into inquiries, some stopping rules prescribing optional stopping are unacceptable to both Bayesians and frequentists.
This paper developed out of discussions with Deborah Mayo. I thank both her and Merrilee Salmon for commenting on earlier drafts. I also thank Teddy Seidenfeld for his comments and guidance on recent drafts.