Nonstandard Errors

Albert J. Menkveld*, Ulf Nielsson, Oliver-Alexander Press, Anders Bjerre Trolle, Stefan Voigt

*Corresponding author for this work

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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—nonstandard 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 more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Original languageEnglish
JournalJournal of Finance
Issue number3
Pages (from-to)2339-2390
Number of pages52
Publication statusPublished - Jun 2024

Bibliographical note

343 authors have contributed to this article.
The authors included in this registration are the corresponding author, CBS-affiliated authors, as well as authors from other Danish universities.

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