Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247663 
Authorgroup: 
Finance Crowd Analysis Project (#fincap)
Year of Publication: 
2021
Series/Report no.: 
SAFE Working Paper No. 327
Publisher: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
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
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
629.78 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.