• Media type: Report; E-Book
  • Title: Non-standard errors
  • Contributor: Menkveld, Albert J. [Author]; Dreber, Anna [Author]; Holzmeister, Felix [Author]; Huber, Jürgen [Author]; Johannesson, Magnus [Author]; Kirchler, Michael [Author]; Neusüss, Sebastian [Author]; Razen, Michael [Author]; Weitzel, Utz [Author]
  • Published: Frankfurt a. M.: Leibniz Institute for Financial Research SAFE, 2021
  • Language: English
  • DOI: https://doi.org/10.2139/ssrn.3961574
  • Keywords: C12 ; non-standard errors ; multi-analyst approach ; liquidity ; G1 ; C18 ; G14
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: 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.
  • Access State: Open Access