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# scipy.stats.ttest_ind

`scipy.stats.``ttest_ind`(a, b, axis=0, equal_var=True, nan_policy='propagate')

  Calculates 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, b : array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axis : int or None, optional Axis along which to compute test. If None, compute over the whole arrays, a, and b. equal_var : bool, optional If True (default), perform a standard independent 2 sample test that assumes equal population variances [R643]. If False, perform Welch’s t-test, which does not assume equal population variance [R644]. New in version 0.11.0. nan_policy : {‘propagate’, ‘raise’, ‘omit’}, optional Defines how to handle when input contains nan. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’.
  Returns: | statistic : float or array The calculated t-statistic. pvalue : float or array The 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

  [R643] | (1, 2) http://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test

  [R644] | (1, 2) http://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)