Non-standard errors

Media type: 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]; Abad Díaz, David [Author]; Abudy, Menachem Meni [Author]; Adrian, Tobias [Author]; Aït-Sahalia, Yacine [Author]; Akmansoy, Olivier [Author]; Alcock, Jamie T. [Author]; Alexeev, Vitali [Author]; Aloosh, Arash [Author]; Amato, Livia [Author]; Amaya, Diego [Author]; Angel, James Joseph [Author]; Avetikian, Alejandro T. [Author]; Bach, Amadeus [Author]; Baidoo, Edwin [Author]; Bakalli, Gaetan [Author]; Bao, Li [Author]; Barbon, Andrea [Author]; Bashchenko, Oksana [Author]; Bindra, Parampreet Christopher [Author]; Bjønnes, Geir H. [Author]

Published: Geneva: Swiss Finance Institute, 2021

Extent: 1 Online-Ressource (circa 58 Seiten); Illustrationen

Language: English

DOI: 10.2139/ssrn.3961574

Identifier:

Keywords: Statistischer Fehler ; Streuungsmaß ; Wissenschaftler ; Wissenschaftliche Methode ; Theorie ; non-standard errors ; multi-analyst approach ; liquidity ; Graue Literatur

Origination:

Footnote:

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

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