scipy.stats.ttest |
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Calculate the T-test for the means of two independent samples of scores. This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the populations have identical variances by default. Parameters a, barray_likeThe arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axisint or None, optionalAxis along which to compute test. If None, compute over the whole arrays, a, and b. equal_varbool, optionalIf True (default), perform a standard independent 2 sample test that assumes equal population variances [1]. If False, perform Welch’s t-test, which does not assume equal population variance [2]. New in version 0.11.0. nan_policy{‘propagate’, ‘raise’, ‘omit’}, optionalDefines how to handle when input contains nan. The following options are available (default is ‘propagate’): ‘propagate’: returns nan ‘raise’: throws an error ‘omit’: performs the calculations ignoring nan values alternative{‘two-sided’, ‘less’, ‘greater’}, optionalDefines the alternative hypothesis. The following options are available (default is ‘two-sided’): ‘two-sided’ ‘less’: one-sided ‘greater’: one-sided New in version 1.6.0. Returns statisticfloat or arrayThe calculated t-statistic. pvaluefloat or arrayThe two-tailed p-value. Notes We can use this test, if we observe two independent samples from the same or different population, e.g. exam scores of boys and girls or of two ethnic groups. The test measures whether the average (expected) value differs significantly across samples. If we observe a large p-value, for example larger than 0.05 or 0.1, then we cannot reject the null hypothesis of identical average scores. If the p-value is smaller than the threshold, e.g. 1%, 5% or 10%, then we reject the null hypothesis of equal averages. References 1https://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test 2https://en.wikipedia.org/wiki/Welch%27s_t-test Examples >>> from scipy import stats >>> np.random.seed(12345678)Test with sample with identical means: >>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500) >>> rvs2 = stats.norm.rvs(loc=5,scale=10,size=500) >>> stats.ttest_ind(rvs1,rvs2) (0.26833823296239279, 0.78849443369564776) >>> stats.ttest_ind(rvs1,rvs2, equal_var = False) (0.26833823296239279, 0.78849452749500748)ttest_ind underestimates p for unequal variances: >>> rvs3 = stats.norm.rvs(loc=5, scale=20, size=500) >>> stats.ttest_ind(rvs1, rvs3) (-0.46580283298287162, 0.64145827413436174) >>> stats.ttest_ind(rvs1, rvs3, equal_var = False) (-0.46580283298287162, 0.64149646246569292)When n1 != n2, the equal variance t-statistic is no longer equal to the unequal variance t-statistic: >>> rvs4 = stats.norm.rvs(loc=5, scale=20, size=100) >>> stats.ttest_ind(rvs1, rvs4) (-0.99882539442782481, 0.3182832709103896) >>> stats.ttest_ind(rvs1, rvs4, equal_var = False) (-0.69712570584654099, 0.48716927725402048)T-test with different means, variance, and n: >>> rvs5 = stats.norm.rvs(loc=8, scale=20, size=100) >>> stats.ttest_ind(rvs1, rvs5) (-1.4679669854490653, 0.14263895620529152) >>> stats.ttest_ind(rvs1, rvs5, equal_var = False) (-0.94365973617132992, 0.34744170334794122) |
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