Nonstandard Errors
MENKVELD, ALBERT J DREBER, ANNA HOLZMEISTER, FELIX HUBER, JUERGEN JOHANNESSON, MAGNUS KIRCHLER, MICHAEL NEUSÜß, SEBASTIAN RAZEN, MICHAEL WEITZEL, UTZ ABAD‐DÍAZ, DAVID
MENKVELD, ALBERT J
DREBER, ANNA
HOLZMEISTER, FELIX
HUBER, JUERGEN
JOHANNESSON, MAGNUS
KIRCHLER, MICHAEL
NEUSÜß, SEBASTIAN
RAZEN, MICHAEL
WEITZEL, UTZ
ABAD‐DÍAZ, DAVID
Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2024
Journal
The Journal of Finance
Book
Publication Volume
79
Publication Issue
3
Publication Begin page
2339
Publication End page
2390
Publication Number of pages
Collections
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.
Research Projects
Organizational Units
Journal Issue
Keywords
Economics, Applied Economics