Non-Standard Errors*

Mathijs van Dijk, Albert J. Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Sebastian Neususs, Michael Razen, Wolf Wagner, Patrick Verwijmeren, Sebastian Vogel, Michel van der Wel, Francesco Mazzola, Antti Yang, Chen Zhou

Research output: Contribution to journalArticleAcademicpeer-review

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 evidencegenerating 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.
Original languageEnglish
Number of pages69
JournalJournal of Finance
DOIs
Publication statusPublished - Jun 2024

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