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| In statistics samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample 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. 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. |
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