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It has been evaluated and determined to be more powerful than the comparable C-test, sometimes referred to as the Poisson exact test." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "Array API Standard Support" } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "CrossRef", "__tag": 4002, "value": "poisson_means_test", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._hypotests:poisson_means_test" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. 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It has\npreviously been established that the number of dodder seeds in clover\nfollows the Poisson distribution.\n\nA 100 gram sample is drawn from the sack before being shipped to the\ngardener. The sample is analyzed, and it is found to contain no dodder\nseeds; that is, `k1` is 0. However, upon arrival, the gardener draws\nanother 100 gram sample from the sack. This time, three dodder seeds are\nfound in the sample; that is, `k2` is 3. The gardener would like to\nknow if the difference is significant and not due to chance. The\nnull hypothesis is that the difference between the two samples is merely\ndue to chance, or that :math:`\\lambda_1 - \\lambda_2 = \\mathtt{diff}`\nwhere :math:`\\mathtt{diff} = 0`. The alternative hypothesis is that the\ndifference is not due to chance, or :math:`\\lambda_1 - \\lambda_2 \\ne 0`.\nThe gardener selects a significance level of 5% to reject the null\nhypothesis in favor of the alternative [2]_.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "import scipy.stats as stats\nres = stats.poisson_means_test(0, 100, 3, 100)\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "res.statistic, res.pvalue\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nThe p-value is .088, indicating a near 9% chance of observing a value of\nthe test statistic under the null hypothesis. 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[1] Krishnamoorthy, K., & Thomson, J. (2004). A more powerful test for", " comparing two Poisson means. Journal of Statistical Planning and", " Inference, 119(1), 23-35.", "", ".. [2] Przyborowski, J., & Wilenski, H. (1940). Homogeneity of results in", " testing samples from Poisson series: With an application to testing", " clover seed for dodder. Biometrika, 31(3/4), 313-323." ], "qa": "scipy.stats._hypotests:poisson_means_test", "arbitrary": [], "local_refs": [ "alternative", "diff", "k1", "k2", "n1: float", "n2", "pvalue", "statistic" ] }