Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/247663 
Autor:innengruppe: 
Finance Crowd Analysis Project (#fincap)
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
SAFE Working Paper No. 327
Verlag: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
non-standard errors
multi-analyst approach
liquidity
JEL: 
C12
C18
G1
G14
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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