Compute the Kruskal-Wallis H-test for independent samples
- sample1, sample2, …array_like
Two or more arrays with the sample measurements can be given as arguments.
The Kruskal-Wallis H statistic, corrected for ties
The p-value for the test using the assumption that H has a chi square distribution
For more details on
kruskal, see stats.kruskal.
>>> from scipy.stats.mstats import kruskal
Random samples from three different brands of batteries were tested to see how long the charge lasted. Results were as follows:
>>> a = [6.3, 5.4, 5.7, 5.2, 5.0] >>> b = [6.9, 7.0, 6.1, 7.9] >>> c = [7.2, 6.9, 6.1, 6.5]
Test the hypotesis that the distribution functions for all of the brands’ durations are identical. Use 5% level of significance.
>>> kruskal(a, b, c) KruskalResult(statistic=7.113812154696133, pvalue=0.028526948491942164)
The null hypothesis is rejected at the 5% level of significance because the returned p-value is less than the critical value of 5%.