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Non-Standard Errors

Sophie Moinas, Fany Declerck, Albert Menkveld and Anna Dreber

No 23-1451, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is gener-ated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Date: 2023-06-29
New Economics Papers: this item is included in nep-exp
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Citations: View citations in EconPapers (3)

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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
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