Why optional stopping can be a problem for Bayesians

TL;DR

This paper investigates the conditions under which optional stopping affects Bayesian hypothesis testing, revealing that common default priors can lead to issues with optional stopping in practical applications.

Contribution

It extends previous experiments to show how different default priors influence the robustness of Bayesian methods against optional stopping.

Findings

Resilience to optional stopping varies with prior type.

Type 0 priors are unaffected by optional stopping.

Type II priors can be problematic with optional stopping.

Abstract

Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder (2014) argues that optional stopping is no problem for Bayesians, and even recommends the use of optional stopping in practice, as do Wagenmakers et al. (2012). This article addresses the question whether optional stopping is problematic for Bayesian methods, and specifies under which circumstances and in which sense it is and is not. By slightly varying and extending Rouder's (2014) experiments, we illustrate that, as soon as the parameters of interest are equipped with default or pragmatic priors - which means, in most practical applications of Bayes factor hypothesis testing - resilience to optional stopping can break down. We distinguish between three types of default priors, each having their own specific issues with optional stopping, ranging from no-problem-at-all (Type 0 priors) to quite severe (Type II priors).