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Non-Standard Errors*

Ciril Bosch-Rosa and Bernhard Kassner
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Ciril Bosch-Rosa: TU Berlin

Authors registered in the RePEc Author Service: Marco Alexander Schwarz

No 385, Rationality and Competition Discussion Paper Series from CRC TRR 190 Rationality and Competition

Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in 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 (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Keywords: uncertainty; standard errors; reproducibility; hypotheses (search for similar items in EconPapers)
JEL-codes: C10 C13 C18 (search for similar items in EconPapers)
Date: 2023-02-11
New Economics Papers: this item is included in nep-exp
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Journal Article: Nonstandard Errors (2024) Downloads
Working Paper: Nonstandard errors (2024) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-Standard Errors (2021) Downloads
Working Paper: Non-standard errors (2021) Downloads
Working Paper: Non-standard errors (2021) Downloads
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