Nonstandard errors

Albert J. Menkveld*, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Sebastian Neusüß, Michael Razen, Utz Weitzel, Fincap Team, Fearghal Kearney, Tony Klein, Liangyi Mu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
25 Downloads (Pure)

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—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
Pages (from-to)2339-2390
JournalJournal of Finance
Volume79
Issue number3
Early online date17 Apr 2024
DOIs
Publication statusPublished - Jun 2024

Fingerprint

Dive into the research topics of 'Nonstandard errors'. Together they form a unique fingerprint.

Cite this