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Research Details

Non-Standard Errors

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.

Type

Working Paper

Author(s)

Robert Korajczyk, Dermot Murphy, Albert Menkveld, Anna Dreber, Felix Holzmeister, Jurgen Huber, Magnus Johannesson, Michael Kirchler, Sebastian Neususs, Michael Razen, Utz Weitzel, al. et, et al.

Date Published

2021

Citations

Korajczyk, Robert, Dermot Murphy, Albert Menkveld, Anna Dreber, Felix Holzmeister, Jurgen Huber, Magnus Johannesson, Michael Kirchler, Sebastian Neususs, Michael Razen, Utz Weitzel, al. et, and et al.. 2021. Non-Standard Errors.

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