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Fonction kruskal - module scipy.stats

Signature de la fonction kruskal

def kruskal(*args, nan_policy='propagate') 

Description

kruskal.__doc__

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. Samples must be one-dimensional.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):

          * 'propagate': returns nan
          * 'raise': throws an error
          * 'omit': performs the calculations ignoring nan values

    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. The p-value returned is the survival function of
       the chi square distribution evaluated at H.

    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
    ----------
    .. [1] 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.
    .. [2] https://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=0.2727272727272734, 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)