Module « scipy.stats »

Fonction normaltest - module scipy.stats

Signature de la fonction normaltest

def normaltest(a, axis=0, nan_policy='propagate') 

Description

normaltest.__doc__

Test whether a sample differs from a normal distribution.

    This function tests the null hypothesis that a sample comes
    from a normal distribution.  It is based on D'Agostino and
    Pearson's [1]_, [2]_ test that combines skew and kurtosis to
    produce an omnibus test of normality.

    Parameters
    ----------
    a : array_like
        The array containing the sample to be tested.
    axis : int or None, optional
        Axis along which to compute test. Default is 0. If None,
        compute over the whole array `a`.
    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 or array
        ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
        ``k`` is the z-score returned by `kurtosistest`.
    pvalue : float or array
       A 2-sided chi squared probability for the hypothesis test.

    References
    ----------
    .. [1] D'Agostino, R. B. (1971), "An omnibus test of normality for
           moderate and large sample size", Biometrika, 58, 341-348

    .. [2] D'Agostino, R. and Pearson, E. S. (1973), "Tests for departure from
           normality", Biometrika, 60, 613-622

    Examples
    --------
    >>> from scipy import stats
    >>> rng = np.random.default_rng()
    >>> pts = 1000
    >>> a = rng.normal(0, 1, size=pts)
    >>> b = rng.normal(2, 1, size=pts)
    >>> x = np.concatenate((a, b))
    >>> k2, p = stats.normaltest(x)
    >>> alpha = 1e-3
    >>> print("p = {:g}".format(p))
    p = 8.4713e-19
    >>> if p < alpha:  # null hypothesis: x comes from a normal distribution
    ...     print("The null hypothesis can be rejected")
    ... else:
    ...     print("The null hypothesis cannot be rejected")
    The null hypothesis can be rejected