Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/248784 
Autor:innengruppe: 
Finance Crowd Analysis Project (#fincap)
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. TI 2021-102/IV
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
JEL: 
G1
C12
C18
Dokumentart: 
Working Paper
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