Non-standard errors
In statistics, samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidencegenerating 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.
Year of publication: |
2021
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Authors: | Menkveld, Albert J. ; Dreber, Anna ; Holzmeister, Felix ; Huber, Jürgen ; Johannesson, Magnus ; Kirchler, Michael ; Neusüss, Sebastian ; Razen, Michael ; Weitzel, Utz |
Publisher: |
Amsterdam and Rotterdam : Tinbergen Institute |
Saved in:
freely available
Series: | Tinbergen Institute Discussion Paper ; TI 2021-102/IV |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 1785750275 [GVK] hdl:10419/248784 [Handle] RePEc:tin:wpaper:20210102 [RePEc] |
Classification: | G1 - General Financial Markets ; C12 - Hypothesis Testing ; c18 |
Source: |
Persistent link: https://www.econbiz.de/10012797258