Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282077 
Year of Publication: 
2023
Series/Report no.: 
Discussion Paper No. 385
Publisher: 
Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition, München und Berlin
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: 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.
Subjects: 
uncertainty
standard errors
reproducibility
hypotheses
JEL: 
C13
C18
C10
Document Type: 
Working Paper

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