City Research Online

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 evidencegenerating 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.

Publication Type:	Article
Additional Information:	This is an open access article under the terms of the CreativeCommonsAttribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Authors. The Journal of Finance published by Wiley Periodicals LLC on behalf of American Finance Association
Subjects:	H Social Sciences > HA Statistics
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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