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Probabilistic forecasting of replication studies

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  • Samuel Pawel
  • Leonhard Held

Abstract

Throughout the last decade, the so-called replication crisis has stimulated many researchers to conduct large-scale replication projects. With data from four of these projects, we computed probabilistic forecasts of the replication outcomes, which we then evaluated regarding discrimination, calibration and sharpness. A novel model, which can take into account both inflation and heterogeneity of effects, was used and predicted the effect estimate of the replication study with good performance in two of the four data sets. In the other two data sets, predictive performance was still substantially improved compared to the naive model which does not consider inflation and heterogeneity of effects. The results suggest that many of the estimates from the original studies were inflated, possibly caused by publication bias or questionable research practices, and also that some degree of heterogeneity between original and replication effects should be expected. Moreover, the results indicate that the use of statistical significance as the only criterion for replication success may be questionable, since from a predictive viewpoint, non-significant replication results are often compatible with significant results from the original study. The developed statistical methods as well as the data sets are available in the R package ReplicationSuccess.

Suggested Citation

  • Samuel Pawel & Leonhard Held, 2020. "Probabilistic forecasting of replication studies," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-23, April.
    • Handle: RePEc:plo:pone00:0231416
    DOI: 10.1371/journal.pone.0231416
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    References listed on IDEAS

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    1. Daniele Fanelli, 2009. "How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-11, May.
    2. Tilmann Gneiting, 2008. "Editorial: Probabilistic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 319-321, April.
    3. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    4. L. Held & K. Rufibach & F. Balabdaoui, 2010. "A Score Regression Approach to Assess Calibration of Continuous Probabilistic Predictions," Biometrics, The International Biometric Society, vol. 66(4), pages 1295-1305, December.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. Jesse Chandler & et. al, 2016. "Response to Comment on "Estimating the Reproducibility of Psychological Science"," Mathematica Policy Research Reports cff9c2f16bb544c4bcca530c0, Mathematica Policy Research.
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    Cited by:

    1. Paul Boeck & Michael L. DeKay & Jolynn Pek, 2024. "Adventitious Error and Its Implications for Testing Relations Between Variables and for Composite Measurement Outcomes," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 1055-1073, September.
    2. Heyard, Rachel & Held, Leonhard, 2024. "Meta-regression to explain shrinkage and heterogeneity in large-scale replication projects," MetaArXiv e9nw2, Center for Open Science.

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