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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IWH Discussion Papers ; No. 11/2021

Classification and Topics

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

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Menkveld, Albert J. ; Dreber, Anna ; Holzmeister, Felix ; Huber, Jürgen ; Johannesson, Magnus ; Kirchler, Michael ; Neusüss, Sebastian ; Razen, Michael ; Weitzel, Utz
• Event: Veröffentlichung
• (who): Halle Institute for Economic Research (IWH)
• (where): Halle (Saale)
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/247267
• Last update: 10.03.2025, 11:42 AM CET