Alternatives to the statistical mass confusion of testing for no-effect
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908358220447744 |
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| author | Morgan, Josh L. |
| author_facet | Morgan, Josh L. |
| contents | In cell biology, statistical analysis means testing the hypothesis that there was no effect. This weak form of hypothesis testing neglects effect size, is universally misinterpreted, and is disastrously prone to error when combined with high-throughput cell biology. The solution is for analysis of measurements to start and end with an interpretation of effect size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Alternatives to the statistical mass confusion of testing for no-effect Morgan, Josh L. Other Quantitative Biology In cell biology, statistical analysis means testing the hypothesis that there was no effect. This weak form of hypothesis testing neglects effect size, is universally misinterpreted, and is disastrously prone to error when combined with high-throughput cell biology. The solution is for analysis of measurements to start and end with an interpretation of effect size. |
| title | Alternatives to the statistical mass confusion of testing for no-effect |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2407.07114 |