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If False, perform Welch's t-test, which does not assume equal population variance " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "2" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "alternative", "annotation": "{'two-sided', 'less', 'greater'}, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Defines the alternative hypothesis. 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With this data, we can compute the same result using\n`scipy.stats.ttest_ind`:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "a = np.array([1, 3, 4, 6, 11, 13, 15, 19, 22, 24, 25, 26, 26])\nb = np.array([2, 4, 6, 9, 11, 13, 14, 15, 18, 19, 21])\nfrom scipy.stats import ttest_ind\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "ttest_ind(a, b)\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nSuppose we instead have binary data and would like to apply a t-test to\ncompare the proportion of 1s in two independent groups::\n\n Number of Sample Sample\n Size ones Mean Variance\n Sample 1 150 30 0.2 0.161073\n Sample 2 200 45 0.225 0.175251\n\nThe sample mean :math:`\\hat{p}` is the proportion of ones in the sample\nand the variance for a binary observation is estimated by\n:math:`\\hat{p}(1-\\hat{p})`.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "ttest_ind_from_stats(mean1=0.2, std1=np.sqrt(0.161073), nobs1=150,\n mean2=0.225, std2=np.sqrt(0.175251), nobs2=200)\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nFor comparison, we could compute the t statistic and p-value using\narrays of 0s and 1s and `scipy.stat.ttest_ind`, as above.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "group1 = np.array([1]*30 + [0]*(150-30))\ngroup2 = np.array([1]*45 + [0]*(200-45))\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "ttest_ind(group1, group2)\n", "execution_status": "failure" } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "scipy.stats.ttest_ind", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._stats_py:ttest_ind" }, "kind": "module" }, "descriptions": [], "type": "func" } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "mean1", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "std1", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "nobs1", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "mean2", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "std2", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "nobs2", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "equal_var", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "True" }, { "__type": "SigParam", "__tag": 4030, "name": "alternative", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "two-sided" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "ttest_ind_from_stats" }, "references": [ ".. 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