scipy.stats.kruskal¶

scipy.stats.
kruskal
(*args, **kwargs)[source]¶ Compute the KruskalWallis Htest for independent samples
The KruskalWallis Htest tests the null hypothesis that the population median of all of the groups are equal. It is a nonparametric 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. Posthoc 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 KruskalWallis H statistic, corrected for ties
pvalue : float
The pvalue for the test using the assumption that H has a chi square distribution
See also
f_oneway
 1way ANOVA
mannwhitneyu
 MannWhitney 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
[R660] W. H. Kruskal & W. W. Wallis, “Use of Ranks in OneCriterion Variance Analysis”, Journal of the American Statistical Association, Vol. 47, Issue 260, pp. 583621, 1952. [R661] http://en.wikipedia.org/wiki/KruskalWallis_oneway_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=0.2727272727272734, pvalue=0.6015081344405895)
>>> x = [1, 1, 1] >>> y = [2, 2, 2] >>> z = [2, 2] >>> stats.kruskal(x, y, z) KruskalResult(statistic=7.0, pvalue=0.0301973834223185)