Non-standard errors

Non-standard errors

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 resea...

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Bibliographic Details
Main Authors: MENKVELT, Albert J., DREBER, Anna, et al., YUESHEN, Bart Zhou, PAGNOTTA, Emiliano Sebastian
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
non-standard errors
multi-analyst approach
liquidity
Finance and Financial Management
Management Sciences and Quantitative Methods
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/7633
https://ink.library.smu.edu.sg/context/lkcsb_research/article/8632/viewcontent/Nonstandard_Errors_pvoa_cc_by.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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