Non-Standard Errors

Menkveld, Albert ; Dreber, Anna ; Holzmeister, Felix ; Huber, Jürgen ; Johannesson, Magnus ; Kirchler, Michael ; Razen, Michael ; Weitzel, Utz ; Abad-Días, David ; Abudy, Menachem ; Adrian, Tobias ; Ait-Sahalia, Yacine ; Akmansoy, Olivier ; Alcock, Jamie T. ; Alexeev, Vitali ; Aloosh, Arash ; Amato, Livia ; Amaya, Diego ; Angel, James J. ; Avetikian, Alejandro T. ; Bach, Amadeus ; Baidoo, Edwin ; Bakalli, Gaetan ; Bao, Li ; Barbon, Andrea ; Bashchenko, Oksana ; Bindra, Parampreet C. ; Bjønnes, Geir H. ; Black, Jeffrey R. ; Black, Bernard S. ; et al.

Document Type: Article
Year of publication: 2023
The title of a journal, publication series: The Journal of Finance
Volume: tba
Issue number: tba
Place of publication: Hoboken, NJ [u.a.]
Publishing house: Wiley
ISSN: 0022-1082 , 1540-6261
Related URLs:
Publication language: English
Institution: Business School > Stiftungsprofessur für ABWL (Reichelstein 2018-)
Subject: 330 Economics
Abstract: In statistics, samples are drawn from a population in a datagenerating 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.

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