scipy.stats.normaltest¶
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scipy.stats.
normaltest
(a, axis=0)[source]¶ Tests 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 [R300], [R301] test that combines skew and kurtosis to produce an omnibus test of normality.
Parameters: a : array_like
The array containing the data 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.
Returns: statistic : float or array
s^2 + k^2, where
s
is the z-score returned byskewtest
and k is the z-score returned bykurtosistest
.pvalue : float or array
A 2-sided chi squared probability for the hypothesis test.
References
[R300] (1, 2) D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size,” Biometrika, 58, 341-348 [R301] (1, 2) D’Agostino, R. and Pearson, E. S. (1973), “Testing for departures from normality,” Biometrika, 60, 613-622