A model of multiple hypothesis testing
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866910025837969408 |
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| author | Viviano, Davide Wuthrich, Kaspar Niehaus, Paul |
| author_facet | Viviano, Davide Wuthrich, Kaspar Niehaus, Paul |
| contents | Multiple hypothesis testing practices vary widely, without consensus on which are appropriate when. This paper provides an economic foundation for these practices designed to capture leading examples, such as regulatory approval on the basis of clinical trials. MHT adjustments are appropriate in our framework to the extent that research costs are invariant to the number of hypotheses. Control of average size, as for example via a Bonferroni correction, emerges in the limit case where all costs are fixed; in the opposite limit, where costs vary in proportion to the hypothesis count, no correction is needed. We illustrate implications by calculating explicit critical values using data on actual costs in the drug approval process and in program evaluation research; these suggest that some MHT adjustment is warranted in these applications, but not as much as implied by standard practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_13367 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A model of multiple hypothesis testing Viviano, Davide Wuthrich, Kaspar Niehaus, Paul General Economics Economics Econometrics Multiple hypothesis testing practices vary widely, without consensus on which are appropriate when. This paper provides an economic foundation for these practices designed to capture leading examples, such as regulatory approval on the basis of clinical trials. MHT adjustments are appropriate in our framework to the extent that research costs are invariant to the number of hypotheses. Control of average size, as for example via a Bonferroni correction, emerges in the limit case where all costs are fixed; in the opposite limit, where costs vary in proportion to the hypothesis count, no correction is needed. We illustrate implications by calculating explicit critical values using data on actual costs in the drug approval process and in program evaluation research; these suggest that some MHT adjustment is warranted in these applications, but not as much as implied by standard practice. |
| title | A model of multiple hypothesis testing |
| topic | General Economics Economics Econometrics |
| url | https://arxiv.org/abs/2104.13367 |