Non-Standard Errors

• Authors: Javier Gil-Bazo and Christian Brownlees.
• BSE Working Paper: 110775 | December 21
• Keywords: liquidity , non-standard errors , multi-analyst approach
• JEL codes: C12, C18, G1, G14
• liquidity
• non-standard errors
• multi-analyst approach

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.