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The sceptical Bayes factor for the assessment of replication success

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  • Samuel Pawel
  • Leonhard Held

Abstract

Replication studies are increasingly conducted but there is no established statistical criterion for replication success. We propose a novel approach combining reverse‐Bayes analysis with Bayesian hypothesis testing: a sceptical prior is determined for the effect size such that the original finding is no longer convincing in terms of a Bayes factor. This prior is then contrasted to an advocacy prior (the reference posterior of the effect size based on the original study), and replication success is declared if the replication data favour the advocacy over the sceptical prior at a higher level than the original data favoured the sceptical prior over the null hypothesis. The sceptical Bayes factor is the highest level where replication success can be declared. A comparison to existing methods reveals that the sceptical Bayes factor combines several notions of replicability: it ensures that both studies show sufficient evidence against the null and penalises incompatibility of their effect estimates. Analysis of asymptotic properties and error rates, as well as case studies from the Social Sciences Replication Project show the advantages of the method for the assessment of replicability.

Suggested Citation

  • Samuel Pawel & Leonhard Held, 2022. "The sceptical Bayes factor for the assessment of replication success," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 879-911, July.
    • Handle: RePEc:bla:jorssb:v:84:y:2022:i:3:p:879-911
    DOI: 10.1111/rssb.12491
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    References listed on IDEAS

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    1. Leonhard Held, 2020. "A new standard for the analysis and design of replication studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 431-448, February.
    2. Maya B. Mathur & Tyler J. VanderWeele, 2020. "New statistical metrics for multisite replication projects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1145-1166, June.
    3. Maxime Derex & Marie-Pauline Beugin & Bernard Godelle & Michel Raymond, 2013. "Experimental evidence for the influence of group size on cultural complexity," Nature, Nature, vol. 503(7476), pages 389-391, November.
    4. Camerer, Colin & Dreber, Anna & Forsell, Eskil & Ho, Teck-Hua & Huber, Jurgen & Johannesson, Magnus & Kirchler, Michael & Almenberg, Johan & Altmejd, Adam & Chan, Taizan & Heikensten, Emma & Holzmeist, 2016. "Evaluating replicability of laboratory experiments in Economics," MPRA Paper 75461, University Library of Munich, Germany.
    5. Alexander Etz & Joachim Vandekerckhove, 2016. "A Bayesian Perspective on the Reproducibility Project: Psychology," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-12, February.
    6. Larry V. Hedges & Jacob M. Schauer, 2019. "More Than One Replication Study Is Needed for Unambiguous Tests of Replication," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 543-570, October.
    7. Valen E. Johnson & Richard D. Payne & Tianying Wang & Alex Asher & Soutrik Mandal, 2017. "On the Reproducibility of Psychological Science," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 1-10, January.
    8. Christopher Harms, 2019. "A Bayes Factor for Replications of ANOVA Results," The American Statistician, Taylor & Francis Journals, vol. 73(4), pages 327-339, October.
    9. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
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