How to interpret p-value of Kolmogorov-Smirnov test (python)? I have Two samples that I want to test (using python) if they are drawn from the same distribution. To do that I use the statistical function ks_2samp from scipy.stats. It returns 2 values and I find difficulties how to interpret them. 
Help please!
 A: When doing a Google search for ks_2samp, the first hit is this website. On it, you can see the function specification:
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

A: As Stijn pointed out, the k-s test returns a D statistic and a p-value corresponding to the D statistic. The D statistic is the absolute max distance (supremum) between the CDFs of the two samples. The closer this number is to 0 the more likely it is that the two samples were drawn from the same distribution. Check out the Wikipedia page for the k-s test. It provides a good explanation: https://en.m.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
The p-value returned by the k-s test has the same interpretation as other p-values. You reject the null hypothesis that the two samples were drawn from the same distribution if the p-value is less than your significance level. You can find tables online for the conversion of the D statistic into a p-value if you are interested in the procedure.
