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 the peer reviewed version of the following article: Menkveld, A. J., Dreber, A., Holzmeister, F. , Huber, J., Johanneson, M., Kirchler, M., Neusüss, S., Razen, M., Weitzel, U., Franus, T. et al. Non-Standard Errors. The Journal of Finance which will be published in final form at https://onlinelibrary.wiley.com/journal/15406261. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Subjects:	H Social Sciences > HA Statistics
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

Export

Downloads