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        "author": "Sasho Savkov",
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        "description": "# randhy\n_Hypothesis thesting with approximate randomisation_\n\n\n[![CircleCI](https://circleci.com/gh/savkov/randhy.svg?style=svg&circle-token=ff0102ad2d043f5548279e8f48a5fcde2297978b)](https://circleci.com/gh/savkov/randhy)\n[![Maintainability](https://api.codeclimate.com/v1/badges/c21544109986302622de/maintainability)](https://codeclimate.com/github/savkov/randhy/maintainability)\n[![Test Coverage](https://api.codeclimate.com/v1/badges/c21544109986302622de/test_coverage)](https://codeclimate.com/github/savkov/randhy/test_coverage)\n\nApproximate randomisation is a significance testing approach suitable for NLP\nproblems.\n\n### \ud83e\udd14 Why not a traditional t-test?\n\nWhile randomisation tests are just as good as analytical approaches such as the \n_t-test_, they are better when the assumptions of the latter are not met and\nthey are also quite simple to implement.\n\n### \ud83d\udda5\ufe0f Installation\n\n```bash\npip install randhy\n```\n\n### References\n\n1. _[William Morgan, Statistical Hypothesis Tests for NLP - Stanford Computer Science (slides)](https://cs.stanford.edu/people/wmorgan/sigtest.pdf)_\n2. _[Wassily Hoeffding. 1952. The Large-Sample Power of Tests Based on Permutations of Observations. Annals ofMathematical Statistics, 23, 169\u2013192.](https://www.jstor.org/stable/2958014?seq=1#page_scan_tab_contents)_\n3. _[Eric W. Noreen. 1989. Computer Intensive Methods forTesting Hypothesis. John Wiley & Sons](https://www.amazon.co.uk/Computer-Intensive-Methods-Testing-Hypotheses-Introduction/dp/0471611360)_\n\n\n",
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