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| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | Non-Standard Errors |
| Organization Unit | |
| Authors |
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| Language |
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| Institution | University of Zurich |
| Series Name | Swiss Finance Institute Research Paper |
| Number | 22-09 |
| Date | 2022 |
| Abstract Text | 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. |
| Official URL | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3961574 |
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