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On the Reproducibility of Psychological Science

Author

Listed:
  • Valen E. Johnson
  • Richard D. Payne
  • Tianying Wang
  • Alex Asher
  • Soutrik Mandal

Abstract

Investigators from a large consortium of scientists recently performed a multi-year study in which they replicated 100 psychology experiments. Although statistically significant results were reported in 97% of the original studies, statistical significance was achieved in only 36% of the replicated studies. This article presents a reanalysis of these data based on a formal statistical model that accounts for publication bias by treating outcomes from unpublished studies as missing data, while simultaneously estimating the distribution of effect sizes for those studies that tested nonnull effects. The resulting model suggests that more than 90% of tests performed in eligible psychology experiments tested negligible effects, and that publication biases based on p-values caused the observed rates of nonreproducibility. The results of this reanalysis provide a compelling argument for both increasing the threshold required for declaring scientific discoveries and for adopting statistical summaries of evidence that account for the high proportion of tested hypotheses that are false. Supplementary materials for this article are available online.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:517:p:1-10
    DOI: 10.1080/01621459.2016.1240079
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    Citations

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    Cited by:

    1. Dreber, Anna & Johannesson, Magnus, 2023. "A framework for evaluating reproducibility and replicability in economics," I4R Discussion Paper Series 38, The Institute for Replication (I4R).
    2. Oliver Braganza, 2020. "A simple model suggesting economically rational sample-size choice drives irreproducibility," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-19, March.
    3. 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.
    4. Forsell, Eskil & Viganola, Domenico & Pfeiffer, Thomas & Almenberg, Johan & Wilson, Brad & Chen, Yiling & Nosek, Brian A. & Johannesson, Magnus & Dreber, Anna, 2019. "Predicting replication outcomes in the Many Labs 2 study," Journal of Economic Psychology, Elsevier, vol. 75(PA).
    5. Bak-Coleman, Joseph B & Mann, Richard P. & West, Jevin & Bergstrom, Carl T., 2022. "Replication does not measure scientific productivity," SocArXiv rkyf7, Center for Open Science.
    6. Ádám Kun, 2018. "Publish and Who Should Perish: You or Science?," Publications, MDPI, vol. 6(2), pages 1-16, April.
    7. Maya B. Mathur & Tyler J. VanderWeele, 2020. "Sensitivity analysis for publication bias in meta‐analyses," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1091-1119, November.
    8. Jeff Miller & Rolf Ulrich, 2019. "The quest for an optimal alpha," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.
    9. Roberta Paroli & Guido Consonni, 2020. "Objective Bayesian comparison of order-constrained models in contingency tables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 139-165, March.

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