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# scipy.stats.ks_2samp

`scipy.stats.``ks_2samp`(data1, data2)

  Computes the Kolmogorov-Smirnof statistic on 2 samples.

  This is a two-sided test for the null hypothesis that 2 independent samples are drawn from the same continuous distribution.

  Parameters : | a, b : sequence of 1-D ndarrays two arrays of sample observations assumed to be drawn from a continuous distribution, sample sizes can be different
  Returns : | D : float KS statistic p-value : float two-tailed p-value

  Notes

  This tests whether 2 samples are drawn from the same distribution. Note that, like in the case of the one-sample K-S test, the distribution is assumed to be continuous.

  This is the two-sided test, one-sided tests are not implemented. The test uses the two-sided asymptotic Kolmogorov-Smirnov distribution.

  If the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same.

  Examples

      >>> from scipy import stats
      >>> np.random.seed(12345678)  #fix random seed to get the same result
      >>> n1 = 200  # size of first sample
      >>> n2 = 300  # size of second sample

  For a different distribution, we can reject the null hypothesis since the pvalue is below 1%:

      >>> rvs1 = stats.norm.rvs(size=n1, loc=0., scale=1)
      >>> rvs2 = stats.norm.rvs(size=n2, loc=0.5, scale=1.5)
      >>> stats.ks_2samp(rvs1, rvs2)
      (0.20833333333333337, 4.6674975515806989e-005)

  For a slightly different distribution, we cannot reject the null hypothesis at a 10% or lower alpha since the p-value at 0.144 is higher than 10%

      >>> rvs3 = stats.norm.rvs(size=n2, loc=0.01, scale=1.0)
      >>> stats.ks_2samp(rvs1, rvs3)
      (0.10333333333333333, 0.14498781825751686)

  For an identical distribution, we cannot reject the null hypothesis since the p-value is high, 41%:

      >>> rvs4 = stats.norm.rvs(size=n2, loc=0.0, scale=1.0)
      >>> stats.ks_2samp(rvs1, rvs4)
      (0.07999999999999996, 0.41126949729859719)

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