Non-Standard Errors
Series
School of Finance Working Paper
Type
forthcoming
Date Issued
2023-03-29
Author(s)
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 acrossresearchers 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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SOF - System-wide Risk in the Financial System
Refereed
Yes
Publisher
Journal of Finance
Volume
2023
Subject(s)
Division(s)
Eprints ID
265822
File(s)![Thumbnail Image]()
Name
21_17_Multi Autors_Non-Standard Errors.pdf
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
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