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Non-standard errors

Gerardo Ferrara () and Simon Jurkatis
Authors registered in the RePEc Author Service: Thomas P. Gehrig

No 955, Bank of England working papers from Bank of England

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.

Keywords: Non-standard errors; multi-analyst approach; liquidity (search for similar items in EconPapers)
JEL-codes: C12 C18 G01 G14 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2021-12-17
New Economics Papers: this item is included in nep-exp
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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
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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