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# scipy.stats.kstest

`scipy.stats.``kstest`(rvs, cdf, args=(), N=20, alternative='two_sided', mode='approx', **kwds)

  Return the D-value and the p-value for a Kolmogorov-Smirnov test

  This performs a test of the distribution G(x) of an observed random variable against a given distribution F(x). Under the null hypothesis the two distributions are identical, G(x)=F(x). The alternative hypothesis can be either ‘two_sided’ (default), ‘less’ or ‘greater’. The KS test is only valid for continuous distributions.

  Parameters: | rvs : string or array or callable string: name of a distribution in scipy.stats array: 1-D observations of random variables callable: function to generate random variables, requires keyword argument size cdf : string or callable string: name of a distribution in scipy.stats, if rvs is a string then cdf can evaluate to False or be the same as rvs callable: function to evaluate cdf args : tuple, sequence distribution parameters, used if rvs or cdf are strings N : int sample size if rvs is string or callable alternative : ‘two_sided’ (default), ‘less’ or ‘greater’ defines the alternative hypothesis (see explanation) mode : ‘approx’ (default) or ‘asymp’ defines the distribution used for calculating p-value ‘approx’ : use approximation to exact distribution of test statistic ‘asymp’ : use asymptotic distribution of test statistic
  Returns: | D : float KS test statistic, either D, D+ or D- p-value : float one-tailed or two-tailed p-value

  Notes

  In the two one-sided test, the alternative is that the empirical cumulative distribution function of the random variable is “less” or “greater” then the cumulative distribution function F(x) of the hypothesis, G(x)<=F(x), resp. G(x)>=F(x).

  If the p-value is greater than the significance level (say 5%), then we cannot reject the hypothesis that the data come from the given distribution.

  Examples

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

      >>> x = np.linspace(-15,15,9)
      >>> kstest(x,'norm')
      (0.44435602715924361, 0.038850142705171065)

      >>> np.random.seed(987654321) # set random seed to get the same result
      >>> kstest('norm','',N=100)
      (0.058352892479417884, 0.88531190944151261)

  is equivalent to this

      >>> np.random.seed(987654321)
      >>> kstest(stats.norm.rvs(size=100),'norm')
      (0.058352892479417884, 0.88531190944151261)

  Test against one-sided alternative hypothesis:

      >>> np.random.seed(987654321)

  Shift distribution to larger values, so that cdf_dgp(x)< norm.cdf(x):

      >>> x = stats.norm.rvs(loc=0.2, size=100)
      >>> kstest(x,'norm', alternative = 'less')
      (0.12464329735846891, 0.040989164077641749)

  Reject equal distribution against alternative hypothesis: less

      >>> kstest(x,'norm', alternative = 'greater')
      (0.0072115233216311081, 0.98531158590396395)

  Don’t reject equal distribution against alternative hypothesis: greater

      >>> kstest(x,'norm', mode='asymp')
      (0.12464329735846891, 0.08944488871182088)

  Testing t distributed random variables against normal distribution:

  With 100 degrees of freedom the t distribution looks close to the normal distribution, and the kstest does not reject the hypothesis that the sample came from the normal distribution

      >>> np.random.seed(987654321)
      >>> stats.kstest(stats.t.rvs(100,size=100),'norm')
      (0.072018929165471257, 0.67630062862479168)

  With 3 degrees of freedom the t distribution looks sufficiently different from the normal distribution, that we can reject the hypothesis that the sample came from the normal distribution at a alpha=10% level

      >>> np.random.seed(987654321)
      >>> stats.kstest(stats.t.rvs(3,size=100),'norm')
      (0.131016895759829, 0.058826222555312224)

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