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Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015

Author

Listed:
  • Colin F. Camerer

    (California Institute of Technology)

  • Anna Dreber

    (Stockholm School of Economics)

  • Felix Holzmeister

    (University of Innsbruck)

  • Teck-Hua Ho

    (National University of Singapore)

  • Jürgen Huber

    (University of Innsbruck)

  • Magnus Johannesson

    (Stockholm School of Economics)

  • Michael Kirchler

    (University of Innsbruck
    University of Göteborg)

  • Gideon Nave

    (University of Pennsylvania)

  • Brian A. Nosek

    (University of Virginia
    Center for Open Science)

  • Thomas Pfeiffer

    (New Zealand Institute for Advanced Study)

  • Adam Altmejd

    (Stockholm School of Economics)

  • Nick Buttrick

    (University of Virginia
    Center for Open Science)

  • Taizan Chan

    (National University of Singapore)

  • Yiling Chen

    (Harvard University)

  • Eskil Forsell

    (Spotify Sweden AB)

  • Anup Gampa

    (University of Virginia
    Center for Open Science)

  • Emma Heikensten

    (Stockholm School of Economics)

  • Lily Hummer

    (Center for Open Science)

  • Taisuke Imai

    (LMU Munich)

  • Siri Isaksson

    (Stockholm School of Economics)

  • Dylan Manfredi

    (University of Pennsylvania)

  • Julia Rose

    (University of Innsbruck)

  • Eric-Jan Wagenmakers

    (University of Amsterdam)

  • Hang Wu

    (Harbin Institute of Technology)

Abstract

Being able to replicate scientific findings is crucial for scientific progress1–15. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 201516–36. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.

Suggested Citation

  • Colin F. Camerer & Anna Dreber & Felix Holzmeister & Teck-Hua Ho & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Gideon Nave & Brian A. Nosek & Thomas Pfeiffer & Adam Altmejd & Nick Buttrick , 2018. "Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015," Nature Human Behaviour, Nature, vol. 2(9), pages 637-644, September.
  • Handle: RePEc:nat:nathum:v:2:y:2018:i:9:d:10.1038_s41562-018-0399-z
    DOI: 10.1038/s41562-018-0399-z
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    References listed on IDEAS

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