Non-Standard Errors

Albert Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Sebastian Neusüss, Michael Razen, Utz Weitzel, Edwin Baidoo, Michael Frömmel and Et Al
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Authors registered in the RePEc Author Service: Thomas P. Gehrig, El Mehdi Ferrouhi and Marco Alexander Schwarz

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: 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.

Pages: 57 pages
Date: 2021-11
New Economics Papers: this item is included in nep-exp
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http://wps-feb.ugent.be/Papers/wp_21_1032.pdf (application/pdf)

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Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-standard errors (2021)
Working Paper: Non-standard errors (2021)
Working Paper: Non-standard errors (2021)
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