The lack of reproducibility in research: How statistics can endorse results
Scientific research is validated by reproduction of the results, but efforts to reproduce spurious claims drain resources. We focus on one cause of such failure: false positive statistical test results caused by random variability. Classical statistical methods rely on p-values to measure the eviden...
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| Format: | Article |
| Language: | Catalan |
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Universitat de València
2015-04-01
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| Series: | Mètode Science Studies Journal: Annual Review |
| Subjects: | |
| Online Access: | https://turia.uv.es/index.php/Metode/article/view/3913 |
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| _version_ | 1846164078621360128 |
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| author | Scott Goddard Valen Johnson |
| author_facet | Scott Goddard Valen Johnson |
| author_sort | Scott Goddard |
| collection | DOAJ |
| description | Scientific research is validated by reproduction of the results, but efforts to reproduce spurious claims drain resources. We focus on one cause of such failure: false positive statistical test results caused by random variability. Classical statistical methods rely on p-values to measure the evidence against null hypotheses, but Bayesian hypothesis testing produces more easily understood results, provided one can specify prior distributions under the alternative hypothesis. We describe new tests, UMPBTs, which are Bayesian tests that provide default specification of alternative priors, and show that these tests also maximize statistical power.
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| format | Article |
| id | doaj-art-c50a514203d347ab83e480fdb7f5ebb6 |
| institution | Kabale University |
| issn | 2174-3487 2174-9221 |
| language | Catalan |
| publishDate | 2015-04-01 |
| publisher | Universitat de València |
| record_format | Article |
| series | Mètode Science Studies Journal: Annual Review |
| spelling | doaj-art-c50a514203d347ab83e480fdb7f5ebb62024-11-18T16:02:56ZcatUniversitat de ValènciaMètode Science Studies Journal: Annual Review2174-34872174-92212015-04-01510.7203/metode.0.3913The lack of reproducibility in research: How statistics can endorse resultsScott Goddard0Valen Johnson1<p>Texas A&M Universitiy (USA).</p><p>Texas A&M Universitiy (USA).</p>Scientific research is validated by reproduction of the results, but efforts to reproduce spurious claims drain resources. We focus on one cause of such failure: false positive statistical test results caused by random variability. Classical statistical methods rely on p-values to measure the evidence against null hypotheses, but Bayesian hypothesis testing produces more easily understood results, provided one can specify prior distributions under the alternative hypothesis. We describe new tests, UMPBTs, which are Bayesian tests that provide default specification of alternative priors, and show that these tests also maximize statistical power. https://turia.uv.es/index.php/Metode/article/view/3913statistical evidencehypothesis testBayesian analysisuniformly most powerful Bayesian tests |
| spellingShingle | Scott Goddard Valen Johnson The lack of reproducibility in research: How statistics can endorse results Mètode Science Studies Journal: Annual Review statistical evidence hypothesis test Bayesian analysis uniformly most powerful Bayesian tests |
| title | The lack of reproducibility in research: How statistics can endorse results |
| title_full | The lack of reproducibility in research: How statistics can endorse results |
| title_fullStr | The lack of reproducibility in research: How statistics can endorse results |
| title_full_unstemmed | The lack of reproducibility in research: How statistics can endorse results |
| title_short | The lack of reproducibility in research: How statistics can endorse results |
| title_sort | lack of reproducibility in research how statistics can endorse results |
| topic | statistical evidence hypothesis test Bayesian analysis uniformly most powerful Bayesian tests |
| url | https://turia.uv.es/index.php/Metode/article/view/3913 |
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