Psychonomic Bulletin & Review

, Volume 21, Issue 2, pp 301–308 | Cite as

Optional stopping: No problem for Bayesians

  • Jeffrey N. RouderEmail author


Optional stopping refers to the practice of peeking at data and then, based on the results, deciding whether or not to continue an experiment. In the context of ordinary significance-testing analysis, optional stopping is discouraged, because it necessarily leads to increased type I error rates over nominal values. This article addresses whether optional stopping is problematic for Bayesian inference with Bayes factors. Statisticians who developed Bayesian methods thought not, but this wisdom has been challenged by recent simulation results of Yu, Sprenger, Thomas, and Dougherty (2013) and Sanborn and Hills (2013). In this article, I show through simulation that the interpretation of Bayesian quantities does not depend on the stopping rule. Researchers using Bayesian methods may employ optional stopping in their own research and may provide Bayesian analysis of secondary data regardless of the employed stopping rule. I emphasize here the proper interpretation of Bayesian quantities as measures of subjective belief on theoretical positions, the difference between frequentist and Bayesian interpretations, and the difficulty of using frequentist intuition to conceptualize the Bayesian approach.


Optional stopping Bayesian testing p-hacking Statistics Bayes factors 



This research was supported by National Science Foundation grants BCS-1240359 and SES-102408.


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Copyright information

© Psychonomic Society, Inc. 2014

Authors and Affiliations

  1. 1.Department of Psychological SciencesUniversity of MissouriColumbiaUSA

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