A Bayesian Perspective on the Reproducibility Project: Psychology

PLoS One. 2016 Feb 26;11(2):e0149794. doi: 10.1371/journal.pone.0149794. eCollection 2016.

Abstract

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors-a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis-for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bayes Theorem*
  • Humans
  • Psychology*
  • Publication Bias*
  • Reproducibility of Results
  • Research Design*
  • Sample Size

Grants and funding

This work was partly funded by the National Science Foundation grants #1230118 and #1534472 from the Methods, 335 Measurements, and Statistics panel (www.nsf.gov) and the John Templeton Foundation grant #48192 (www.templeton.org). This publication was made possible through the support of a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.