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Published December 5, 2019 | Supplemental Material + Submitted + Published
Journal Article Open

Predicting the replicability of social science lab experiments


We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman ρ of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists [1, 2]. The predictive power is validated in a pre-registered out of sample test of the outcome of [3], where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to ρ = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative.

Additional Information

© 2019 Altmejd et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: June 20, 2019; Accepted: November 13, 2019; Published: December 5, 2019. Data Availability: All data and code files are available from the OSF project repository (https://osf.io/4fn73/, DOI: 10.17605/OSF.IO/4FN73). Generous support was provided by the Alfred P. Sloan Foundation (G-201513929, to CFC), the Chen Center for Social and Decision Neuroscience (to CFC), the Austrian Science Fund FWF (SFB F63 to MK and JH, P29362-G27 to JH), The Jan Wallander and Tom Hedelius Foundation (P2015-0001:1 to MJ, P14-0214 and P13-0156 to AD), The Knut and Alice Wallenberg Foundation (Wallenberg Academy Fellows grant to AD), The Swedish Foundation for Humanities and Social Sciences (NHS14--1719 to MJ and AD), and Forte (2016-07099 to AA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. We thank Isak Roth and Dylan Manfredi for outstanding research assistance. Author Contributions: Conceptualization: Adam Altmejd. Data curation: Adam Altmejd, Anna Dreber, Eskil Forsell, Juergen Huber, Taisuke Imai, Magnus Johannesson, Michael Kirchler, Gideon Nave. Formal analysis: Adam Altmejd, Eskil Forsell, Taisuke Imai, Gideon Nave. Funding acquisition: Anna Dreber, Magnus Johannesson, Colin Camerer. Investigation: Adam Altmejd. Methodology: Adam Altmejd, Taisuke Imai, Gideon Nave. Project administration: Adam Altmejd. Software: Adam Altmejd. Supervision: Anna Dreber, Magnus Johannesson, Colin Camerer. Validation: Adam Altmejd. Visualization: Adam Altmejd. Writing – original draft: Adam Altmejd. Writing – review & editing: Adam Altmejd, Taisuke Imai, Magnus Johannesson, Michael Kirchler, Gideon Nave, Colin Camerer.

Attached Files

Published - journal.pone.0225826.pdf

Submitted - Predicting_Replication.pdf

Supplemental Material - journal.pone.0225826.s001.pdf

Supplemental Material - journal.pone.0225826.s002.pdf

Supplemental Material - journal.pone.0225826.s003.pdf

Supplemental Material - journal.pone.0225826.s004.pdf

Supplemental Material - journal.pone.0225826.s005.pdf

Supplemental Material - journal.pone.0225826.s006.pdf

Supplemental Material - journal.pone.0225826.s007.eps

Supplemental Material - journal.pone.0225826.s008.eps

Supplemental Material - journal.pone.0225826.s009.eps

Supplemental Material - journal.pone.0225826.s010.eps


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August 19, 2023
October 23, 2023