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Predicting replication outcomes in the Many Labs 2 study

Author

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  • Forsell, Eskil
  • Viganola, Domenico
  • Pfeiffer, Thomas
  • Almenberg, Johan
  • Wilson, Brad
  • Chen, Yiling
  • Nosek, Brian A.
  • Johannesson, Magnus
  • Dreber, Anna

Abstract

Understanding and improving reproducibility is crucial for scientific progress. Prediction markets and related methods of eliciting peer beliefs are promising tools to predict replication outcomes. We invited researchers in the field of psychology to judge the replicability of 24 studies replicated in the large scale Many Labs 2 project. We elicited peer beliefs in prediction markets and surveys about two replication success metrics: the probability that the replication yields a statistically significant effect in the original direction (p < 0.001), and the relative effect size of the replication. The prediction markets correctly predicted 75% of the replication outcomes, and were highly correlated with the replication outcomes. Survey beliefs were also significantly correlated with replication outcomes, but had larger prediction errors. The prediction markets for relative effect sizes attracted little trading and thus did not work well. The survey beliefs about relative effect sizes performed better and were significantly correlated with observed relative effect sizes. The results suggest that replication outcomes can be predicted and that the elicitation of peer beliefs can increase our knowledge about scientific reproducibility and the dynamics of hypothesis testing.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:joepsy:v:75:y:2019:i:pa:s0167487018303283
    DOI: 10.1016/j.joep.2018.10.009
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    Cited by:

    1. Fanelli, Daniele, 2020. "Metascientific reproducibility patterns revealed by informatic measure of knowledge," MetaArXiv 5vnhj, Center for Open Science.
    2. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    3. Alipourfard, Nazanin & Arendt, Beatrix & Benjamin, Daniel Jacob & Benkler, Noam & Bishop, Michael Metcalf & Burstein, Mark & Bush, Martin & Caverlee, James & Chen, Yiling & Clark, Chae, 2021. "Systematizing Confidence in Open Research and Evidence (SCORE)," SocArXiv 46mnb, Center for Open Science.
    4. Frederik Bossaerts & Nitin Yadav & Peter Bossaerts & Chad Nash & Torquil Todd & Torsten Rudolf & Rowena Hutchins & Anne-Louise Ponsonby & Karl Mattingly, 2022. "Price Formation in Field Prediction Markets: the Wisdom in the Crowd," Papers 2209.08778, arXiv.org.
    5. Tierney, Warren & Hardy, Jay H. & Ebersole, Charles R. & Leavitt, Keith & Viganola, Domenico & Clemente, Elena Giulia & Gordon, Michael & Dreber, Anna & Johannesson, Magnus & Pfeiffer, Thomas & Uhlman, 2020. "Creative destruction in science," Organizational Behavior and Human Decision Processes, Elsevier, vol. 161(C), pages 291-309.

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    More about this item

    Keywords

    Reproducibility; Replications; Prediction markets; Beliefs;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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