Non-Standard Errors

• Authors: Christian Brownlees, Anna Dreber, Javier Gil-Bazo, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Albert J. Menkveld, Sebastian Neussüs, Michael Razen and Utz Weitzel
• BSE Working Paper: 1303 | February 2026
• Keywords: liquidity, non-standard errors, multi-analyst approach
• JEL codes: C12, C18, G1, G14

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.