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# scipy.stats.kruskal

`scipy.stats.``kruskal`(*args, **kwargs)

  Compute the Kruskal-Wallis H-test for independent samples

  The Kruskal-Wallis H-test tests the null hypothesis that the population median of all of the groups are equal. It is a non-parametric version of ANOVA. The test works on 2 or more independent samples, which may have different sizes. Note that rejecting the null hypothesis does not indicate which of the groups differs. Post-hoc comparisons between groups are required to determine which groups are different.

  Parameters:

  sample1, sample2, ... : array_like

  > Two or more arrays with the sample measurements can be given as arguments.

  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

  > The Kruskal-Wallis H statistic, corrected for ties

  pvalue : float

  > The p-value for the test using the assumption that H has a chi square distribution

  See also

  `f_oneway`
    1-way ANOVA
  `mannwhitneyu`
    Mann-Whitney rank test on two samples.
  `friedmanchisquare`
    Friedman test for repeated measurements

  Notes

  Due to the assumption that H has a chi square distribution, the number of samples in each group must not be too small. A typical rule is that each sample must have at least 5 measurements.

  References

  [R407] | W. H. Kruskal & W. W. Wallis, “Use of Ranks in One-Criterion Variance Analysis”, Journal of the American Statistical Association, Vol. 47, Issue 260, pp. 583-621, 1952.

  [R408] | http://en.wikipedia.org/wiki/Kruskal-Wallis_one-way_analysis_of_variance

  Examples

      >>> from scipy import stats
      >>> x = [1, 3, 5, 7, 9]
      >>> y = [2, 4, 6, 8, 10]
      >>> stats.kruskal(x, y)
      KruskalResult(statistic=0.27272727272727337, pvalue=0.60150813444058948)

      >>> x = [1, 1, 1]
      >>> y = [2, 2, 2]
      >>> z = [2, 2]
      >>> stats.kruskal(x, y, z)
      KruskalResult(statistic=7.0, pvalue=0.030197383422318501)