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# scipy.stats.mstats.ttest_rel

`scipy.stats.mstats.``ttest_rel`(a, b, axis=None)

  Calculates the T-test on TWO RELATED samples of scores, a and b.

  > This is a two-sided test for the null hypothesis that 2 related or repeated samples have identical average (expected) values.

  Parameters: | a, b : sequence of ndarrays The arrays must have the same shape. axis : int, optional, (default axis=0) Axis can equal None (ravel array first), or an integer (the axis over which to operate on a and b).
  Returns: | t : float or array t-statistic prob : float or array two-tailed p-value

  Notes

  Examples for the use are scores of the same set of student in different exams, or repeated sampling from the same units. The test measures whether the average score differs significantly across samples (e.g. exams). If we observe a large p-value, for example greater than 0.5 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. Small p-values are associated with large t-statistics.

  Examples

      >>> from scipy import stats
      >>> import numpy as np

      >>> #fix random seed to get the same result
      >>> np.random.seed(12345678)
      >>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
      >>> rvs2 = stats.norm.rvs(loc=5,scale=10,size=500) +                             stats.norm.rvs(scale=0.2,size=500)
      >>> stats.ttest_rel(rvs1,rvs2)
      (0.24101764965300962, 0.80964043445811562)
      >>> rvs3 = stats.norm.rvs(loc=8,scale=10,size=500) +                             stats.norm.rvs(scale=0.2,size=500)
      >>> stats.ttest_rel(rvs1,rvs3)
      (-3.9995108708727933, 7.3082402191726459e-005)

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