Publication: Nonstandard errors
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Co-Authors
Menkveld, Albert J.
Dreber, Anna
Holzmeister, Felix
Huber, Juergen
Johannesson, Magnus
Kirchler, Michael
Neususs, Sebastian
Razen, Michael
Weitzel, Utz
Abad-Diaz, David
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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 generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard 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 more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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Publisher
John Wiley and Sons Inc
Citation
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Source
Journal of Finance
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DOI
10.1111/jofi.13337
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CC BY (Attribution)
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Creative Commons license
Except where otherwise noted, this item's license is described as CC BY (Attribution)