Non-Standard Errors

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.

Replaced by

Albert J. Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Michael Razen, Utz Weitzel, Fany Declerck, and Sophie Moinas, “Nonstandard Errors”, The Journal of Finance, vol. 79, n. 3, June 2024, pp. 2339–2390.

Reference

Albert J. Menkveld, Anna Dreber, Fany Declerck, and Sophie Moinas, “Non-Standard Errors”, TSE Working Paper, n. 23-1451, June 2023.