Non-Standard Errors

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Non-Standard Errors. / Voigt, Stefan; Menkveld et al., Albert.

I: The Journal of Finance, 2024.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Voigt, S & Menkveld et al., A 2024, 'Non-Standard Errors', The Journal of Finance. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3961574>

APA

Voigt, S., & Menkveld et al., A. (Accepteret/In press). Non-Standard Errors. The Journal of Finance. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3961574

Vancouver

Voigt S, Menkveld et al. A. Non-Standard Errors. The Journal of Finance. 2024.

Author

Voigt, Stefan ; Menkveld et al., Albert. / Non-Standard Errors. I: The Journal of Finance. 2024.

Bibtex

@article{e7f49d5840074cd5a6f179af0b47e330,
title = "Non-Standard Errors",
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.",
author = "Stefan Voigt and {Menkveld et al.}, Albert",
year = "2024",
language = "English",
journal = "The Journal of Finance",
issn = "0022-1082",
publisher = "Wiley",

}

RIS

TY - JOUR

T1 - Non-Standard Errors

AU - Voigt, Stefan

AU - Menkveld et al., Albert

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

M3 - Journal article

JO - The Journal of Finance

JF - The Journal of Finance

SN - 0022-1082

ER -

ID: 336461074