Published November 24, 2021
| Version v1
Journal article
Open
Non-Standard Errors
Creators
- 1. Vrije Universiteit Amsterdam, Tinbergen Institute
- 2. Stockholm School of Economics
- 3. University of Innsbruck
- 4. Aalto University
- 5. Radboud University, Vrije Universiteit Amsterdam
Contributors
Project members:
- 1. HEC Paris
- 2. Université d'Orléans
- 3. CNRS
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.
Online appendix available at: https://bit.ly/3DIQKrB.
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Non-Standard Errors (SSRN-id3961574).pdf
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