Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/264941 
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. 13/2021
Publisher: 
Norges Bank, Oslo
Abstract: 
In statistics, samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample 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. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Persistent Identifier of the first edition: 
ISBN: 
978-82-8379-210-2
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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