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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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    • 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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