Repository: scipy/scipy
File: scipy/stats/_morestats.py (Python)

Code before:
        The p-value for the hypothesis test.

    See Also
    --------
    bartlett : A parametric test for equality of k variances in normal samples
    levene : A robust parametric test for equality of k variances
    :ref:`hypothesis_fligner` : Extended example

    Notes
    -----
    As with Levene's test there are three variants of Fligner's test that
    differ by the measure of central tendency used in the test.  See `levene`
    for more information.

    Conover et al. (1981) examine many of the existing parametric and
    nonparametric tests by extensive simulations and they conclude that the
    tests proposed by Fligner and Killeen (1976) and Levene (1960) appear to be
    superior in terms of robustness of departures from normality and power
    [1]_.

    References
    ----------
    .. [1] Qu, A., Lindsay, B. G., and Li, B. (2000). Improving generalized 
           estimating equations using quadratic inference functions. 
           Biometrika, 87(4), 823-836. 
           https://doi.org/10.1093/biomet/87.4.823
    .. [2] Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample
           tests for scale. Journal of the American Statistical Association.
           71(353), 210-213.
    .. [3] Conover, W. J., Johnson, M. E. and Johnson M. M. (1981). A
           comparative study of tests for homogeneity of variances, with
           applications to the outer continental shelf bidding data.
           Technometrics, 23(4), 351-361.

    Examples
    --------

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

    Test whether the lists `a`, `b` and `c` come from populations
    with equal variances.

    >>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
    >>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
    >>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
    >>> stat, p = stats.fligner(a, b, c)
    >>> p
    0.00450826080004775

    The small p-value suggests that the populations do not have equal

Review comment (suggestion):
```suggestion
           :doi:`10.1093/biomet/87.4.823`
```

Code after:
        The p-value for the hypothesis test.

    See Also
    --------
    bartlett : A parametric test for equality of k variances in normal samples
    levene : A robust parametric test for equality of k variances
    :ref:`hypothesis_fligner` : Extended example

    Notes
    -----
    As with Levene's test there are three variants of Fligner's test that
    differ by the measure of central tendency used in the test.  See `levene`
    for more information.

    Conover et al. (1981) examine many of the existing parametric and
    nonparametric tests by extensive simulations and they conclude that the
    tests proposed by Fligner and Killeen (1976) and Levene (1960) appear to be
    superior in terms of robustness of departures from normality and power
    [3]_.

    References
    ----------
    .. [1] Qu, A., Lindsay, B. G., and Li, B. (2000). Improving generalized 
           estimating equations using quadratic inference functions. 
           Biometrika, 87(4), 823-836. 
           :doi:`10.1093/biomet/87.4.823`
    .. [2] Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample
           tests for scale. Journal of the American Statistical Association.
           71(353), 210-213.
    .. [3] Conover, W. J., Johnson, M. E. and Johnson M. M. (1981). A
           comparative study of tests for homogeneity of variances, with
           applications to the outer continental shelf bidding data.
           Technometrics, 23(4), 351-361.

    Examples
    --------

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

    Test whether the lists `a`, `b` and `c` come from populations
    with equal variances.

    >>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
    >>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
    >>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
    >>> stat, p = stats.fligner(a, b, c)
    >>> p
    0.00450826080004775

    The small p-value suggests that the populations do not have equal