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Non-Standard Errors

Christian Wolff, Albert Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Neusüess, Sebastian, Michael Razen and Utz Weitzel
Authors registered in the RePEc Author Service: Thomas P. Gehrig

No 16751, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Date: 2021-11
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Related works:
Journal Article: Nonstandard Errors (2024) Downloads
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Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-standard errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-standard errors (2021) Downloads
Working Paper: Non-standard errors (2021) Downloads
Working Paper: Non-standard errors (2021) Downloads
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