Non-standard errors

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SAFE Working Paper ; No. 327

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Hypothesis Testing: General ; Methodological Issues: General ; Information and Market Efficiency; Event Studies; Insider Trading
• Thema: non-standard errors ; multi-analyst approach ; liquidity

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Menkveld, Albert J. ; Dreber, Anna ; Holzmeister, Felix ; Huber, Jürgen ; Johannesson, Magnus ; Kirchler, Michael ; Neusüss, Sebastian ; Razen, Michael ; Weitzel, Utz
• Ereignis: Veröffentlichung
• (wer): Leibniz Institute for Financial Research SAFE
• (wo): Frankfurt a. M.
• (wann): 2021

Weitere Informationen

• DOI: doi:10.2139/ssrn.3961574
• Handle: http://hdl.handle.net/10419/247663
• URN: urn:nbn:de:hebis:30:3-616364
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ