Arbeitspapier
Unpacking P-Hacking and Publication Bias
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.
- Language
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Englisch
- Bibliographic citation
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Series: I4R Discussion Paper Series ; No. 52
- Classification
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Wirtschaft
Role of Economics; Role of Economists; Market for Economists
Estimation: General
Econometric and Statistical Methods: Special Topics: General
- Subject
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publication bias
p-hacking
selective reporting
- Event
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Geistige Schöpfung
- (who)
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Brodeur, Abel
Carrell, Scott
Figlio, David
Lusher, Lester
- Event
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Veröffentlichung
- (who)
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Institute for Replication (I4R)
- (where)
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s.l.
- (when)
-
2023
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Brodeur, Abel
- Carrell, Scott
- Figlio, David
- Lusher, Lester
- Institute for Replication (I4R)
Time of origin
- 2023