Non-Standard Errors

Albert Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Jean-Baptiste Hasse and E.A.,

No 2023002, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence- generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Pages: 62
Date: 2023-01-01
Note: In: Journal of Finance, 2023
References: Add references at CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Nonstandard Errors (2024)
Working Paper: Nonstandard errors (2024)
Working Paper: Nonstandard Errors (2024)
Working Paper: Non-Standard Errors (2023)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-standard errors (2021)
Working Paper: Non-standard errors (2021)
Working Paper: Non-standard errors (2021)
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