Barbon, AndreaAndreaBarbonHolzmeister, FelixFelixHolzmeisterRanaldo, AngeloAngeloRanaldo2023-04-132023-04-132023-03-29https://www.alexandria.unisg.ch/handle/20.500.14171/107646In 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 acrossresearchers 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.enforthcoming