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Do t -Statistic Hurdles Need to Be Raised?

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Τίτλος: Do t -Statistic Hurdles Need to Be Raised?
Συγγραφείς: Chen, Andrew Y.1 (AUTHOR) andrew.y.chen@frb.gov
Πηγή: Management Science (INFORMS). Jul2025, Vol. 71 Issue 7, p5830-5848. 19p.
Θεματικοί όροι: *Empirical research, *Data analysis, False positive error, Scholarly publishing, Empirical Bayes methods, T-test (Statistics)
Περίληψη: Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: Results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the false discovery rate) can be strongly identified, as data on published findings are plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03083. [ABSTRACT FROM AUTHOR]
Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Do t -Statistic Hurdles Need to Be Raised?
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  Data: Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: Results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the false discovery rate) can be strongly identified, as data on published findings are plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03083. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1287/mnsc.2023.03083
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      – Code: eng
        Text: English
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      – SubjectFull: Empirical research
        Type: general
      – SubjectFull: Data analysis
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      – SubjectFull: False positive error
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      – SubjectFull: Empirical Bayes methods
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              Text: Jul2025
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