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Non-Standard Errors

dc.contributor.authorMenkveld, A.
dc.contributor.authorDreber, A.
dc.contributor.authorHolzmeister, F.
dc.contributor.authorHuber, J.
dc.contributor.authorJohannesson, M.
dc.contributor.authorKirchler, M.
dc.contributor.authorNeusüss, S.
dc.contributor.authorRazen, M.
dc.contributor.authorWeitzel, U.
dc.contributor.authorLinton, O.
dc.date.accessioned2022-01-04T17:05:08Z
dc.date.available2022-01-04T17:05:08Z
dc.date.issued2021-11-25
dc.description.abstractIn 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.<br /><br />Online appendix available at <a href="https://bit.ly/3DIQKrB<" target="_blank">https://bit.ly/3DIQKrB</a>.<br /><br />Please note a full list of authors is available in the working paper.
dc.identifier.doi10.17863/CAM.79374
dc.identifier.otherCWPE2182
dc.identifier.otherJIWP2112
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331925
dc.language.isoeng
dc.publisherFaculty of Economics, University of Cambridge
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.relation.ispartofseriesJaneway Institute Working Paper Series
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectMarket Efficiency
dc.subjectP-hacking
dc.subjectPublication bias
dc.titleNon-Standard Errors
dc.typeWorking Paper

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