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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2012
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1201.1431 |
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| _version_ | 1866929304140513280 |
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| author | Perkins, William Tygert, Mark Ward, Rachel |
| author_facet | Perkins, William Tygert, Mark Ward, Rachel |
| contents | Goodness-of-fit tests based on the Euclidean distance often outperform chi-square and other classical tests (including the standard exact tests) by at least an order of magnitude when the model being tested for goodness-of-fit is a discrete probability distribution that is not close to uniform. The present article discusses numerous examples of this. Goodness-of-fit tests based on the Euclidean metric are now practical and convenient: although the actual values taken by the Euclidean distance and similar goodness-of-fit statistics are seldom humanly interpretable, black-box computer programs can rapidly calculate their precise significance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1201_1431 |
| institution | arXiv |
| publishDate | 2012 |
| record_format | arxiv |
| spellingShingle | An introduction to how chi-square and classical exact tests often wildly misreport significance and how the remedy lies in computers Perkins, William Tygert, Mark Ward, Rachel Methodology Computation Goodness-of-fit tests based on the Euclidean distance often outperform chi-square and other classical tests (including the standard exact tests) by at least an order of magnitude when the model being tested for goodness-of-fit is a discrete probability distribution that is not close to uniform. The present article discusses numerous examples of this. Goodness-of-fit tests based on the Euclidean metric are now practical and convenient: although the actual values taken by the Euclidean distance and similar goodness-of-fit statistics are seldom humanly interpretable, black-box computer programs can rapidly calculate their precise significance. |
| title | An introduction to how chi-square and classical exact tests often wildly misreport significance and how the remedy lies in computers |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/1201.1431 |