Non-Standard Errors

Albert J. et al. Menkveld
Authors registered in the RePEc Author Service: Thomas P. Gehrig, Albert J. Menkveld and Marco Alexander Schwarz

No 9453, CESifo Working Paper Series from CESifo

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 G10 G14 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-exp
References: View references in EconPapers View complete reference list from CitEc
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Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp9453.pdf (application/pdf)

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Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-Standard Errors (2021)
Working Paper: Non-standard errors (2021)
Working Paper: Non-standard errors (2021)
Working Paper: Non-standard errors (2021)
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