Non-Standard Errors
Publikation: Vorabdruck/Dokumentation/Bericht › Arbeitspapier
Beitragende
Abstract
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.
Details
Originalsprache | Englisch |
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Publikationsstatus | Veröffentlicht - 23 Nov. 2021 |
Publikationsreihe
Reihe | SSRN eLibrary / Social Science Research Network |
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ISSN | 1556-5068 |
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Externe IDs
ORCID | /0000-0003-4359-987X/work/142255145 |
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Schlagworte
Schlagwörter
- non-standard errors, multi-analyst approach, liquidity