Module « scipy.stats »

Fonction ttest_rel - module scipy.stats

Signature de la fonction ttest_rel

def ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided') 

Description

ttest_rel.__doc__

Calculate the t-test on TWO RELATED samples of scores, a and b.

    This is a two-sided test for the null hypothesis that 2 related or
    repeated samples have identical average (expected) values.

    Parameters
    ----------
    a, b : array_like
        The arrays must have the same shape.
    axis : int or None, optional
        Axis along which to compute test. If None, compute over the whole
        arrays, `a`, and `b`.
    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
    alternative : {'two-sided', 'less', 'greater'}, optional
        Defines the alternative hypothesis.
        The following options are available (default is 'two-sided'):

          * 'two-sided'
          * 'less': one-sided
          * 'greater': one-sided

          .. versionadded:: 1.6.0

    Returns
    -------
    statistic : float or array
        t-statistic.
    pvalue : float or array
        Two-sided p-value.

    Notes
    -----
    Examples for use are scores of the same set of student in
    different exams, or repeated sampling from the same units. The
    test measures whether the average score differs significantly
    across samples (e.g. exams). If we observe a large p-value, for
    example greater than 0.05 or 0.1 then we cannot reject the null
    hypothesis of identical average scores. If the p-value is smaller
    than the threshold, e.g. 1%, 5% or 10%, then we reject the null
    hypothesis of equal averages. Small p-values are associated with
    large t-statistics.

    References
    ----------
    https://en.wikipedia.org/wiki/T-test#Dependent_t-test_for_paired_samples

    Examples
    --------
    >>> from scipy import stats
    >>> rng = np.random.default_rng()

    >>> rvs1 = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
    >>> rvs2 = (stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
    ...         + stats.norm.rvs(scale=0.2, size=500, random_state=rng))
    >>> stats.ttest_rel(rvs1, rvs2)
    Ttest_relResult(statistic=-0.4549717054410304, pvalue=0.6493274702088672)
    >>> rvs3 = (stats.norm.rvs(loc=8, scale=10, size=500, random_state=rng)
    ...         + stats.norm.rvs(scale=0.2, size=500, random_state=rng))
    >>> stats.ttest_rel(rvs1, rvs3)
    Ttest_relResult(statistic=-5.879467544540889, pvalue=7.540777129099917e-09)