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Unpacking P-Hacking and Publication Bias

Author

Listed:
  • Brodeur, Abel
  • Carrell, Scott
  • Figlio, David
  • Lusher, Lester
Abstract
We use unique data from journal submissions to identify and unpack publication bias and p-hacking. We find that initial submissions display significant bunching, suggesting the distribution among published statistics cannot be fully attributed to a publication bias in peer review. Desk-rejected manuscripts display greater heaping than those sent for review i.e. marginally significant results are more likely to be desk rejected. Reviewer recommendations, in contrast, are positively associated with statistical significance. Overall, the peer review process has little effect on the distribution of test statistics. Lastly, we track rejected papers and present evidence that the prevalence of publication biases is perhaps not as prominent as feared.

Suggested Citation

  • Brodeur, Abel & Carrell, Scott & Figlio, David & Lusher, Lester, 2023. "Unpacking P-Hacking and Publication Bias," I4R Discussion Paper Series 52, The Institute for Replication (I4R).
    • Handle: RePEc:zbw:i4rdps:52
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    Cited by:

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    2. Rose, Julian & Neubauer, Florian & Ankel-Peters, Jörg, 2024. "Long-term effects of the targeting the ultra-poor program: A reproducibility and replicability assessment of Banerjee et al. (2021)," Ruhr Economic Papers 1107, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Dreber, Anna & Johannesson, Magnus, 2023. "A framework for evaluating reproducibility and replicability in economics," I4R Discussion Paper Series 38, The Institute for Replication (I4R).
    4. Jordan C. Stanley & Evan S. Totty, 2024. "Synthetic Data and Social Science Research: Accuracy Assessments and Practical Considerations from the SIPP Synthetic Beta," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    5. Danielle V. Handel & Eric A. Hanushek, 2024. "Contexts of Convenience: Generalizing from Published Evaluations of School Finance Policies," Evaluation Review, , vol. 48(3), pages 461-494, June.
    6. Abel Brodeur & Nikolai M. Cook & Jonathan S. Hartley & Anthony Heyes, 2024. "Do Preregistration and Preanalysis Plans Reduce p-Hacking and Publication Bias? Evidence from 15,992 Test Statistics and Suggestions for Improvement," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 2(3), pages 527-561.
    7. Irsova, Zuzana & Doucouliagos, Hristos & Havranek, Tomas & Stanley, T. D., 2023. "Meta-Analysis of Social Science Research: A Practitioner’s Guide," EconStor Preprints 273719, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    publication bias; p-hacking; selective reporting;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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