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Unpacking P-Hacking and Publication Bias

Author

Listed:
  • Abel Brodeur
  • Scott E. Carrell
  • David N. Figlio
  • Lester R. Lusher

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

  • Abel Brodeur & Scott E. Carrell & David N. Figlio & Lester R. Lusher, 2023. "Unpacking P-Hacking and Publication Bias," NBER Working Papers 31548, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31548
    Note: ED LS PE
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    JEL classification:

    • A0 - General Economics and Teaching - - General

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