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How Best to Quantify Replication Success? A Simulation Study on the Comparison of Replication Success Metrics

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  • Muradchanian, Jasmine
  • Hoekstra, Rink
  • Kiers, Henk
  • van Ravenzwaaij, Don

    (University of Groningen)

Abstract

To overcome the frequently debated crisis of confidence, replicating studies is becoming increasingly more common. Multiple frequentist and Bayesian measures have been proposed to evaluate whether a replication is successful, but little is known about which method best captures replication success. We studied this in a simulation study, by comparing a number of quantitative measures of replication success with respect to their ability to draw the correct inference when the underlying truth is known, while taking publication bias into account. Our results show that Bayesian metrics seem to slightly outperform frequentist metrics across the board. Generally, meta-analytic approaches seem to slightly outperform metrics that evaluate single studies, except in the scenario of extreme publication bias, where this pattern reverses.

Suggested Citation

  • Muradchanian, Jasmine & Hoekstra, Rink & Kiers, Henk & van Ravenzwaaij, Don, 2020. "How Best to Quantify Replication Success? A Simulation Study on the Comparison of Replication Success Metrics," MetaArXiv wvdjf, Center for Open Science.
    • Handle: RePEc:osf:metaar:wvdjf
    DOI: 10.31219/osf.io/wvdjf
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    References listed on IDEAS

    as
    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.
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    Cited by:

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