Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247267 
Year of Publication: 
2021
Series/Report no.: 
IWH Discussion Papers No. 11/2021
Publisher: 
Halle Institute for Economic Research (IWH), Halle (Saale)
Abstract: 
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample 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. 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.
Subjects: 
non-standard errors
multi-analyst approach
liquidity
JEL: 
C12
C18
G1
G14
Document Type: 
Working Paper

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