scipy.stats.mstats.normaltest

scipy.stats.mstats.normaltest(a, axis=0)

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 [R340], [R341] 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 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

[R340]

(1, 2) D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size,” Biometrika, 58, 341-348

[R341]

(1, 2) D’Agostino, R. and Pearson, E. S. (1973), “Testing for departures from normality,” Biometrika, 60, 613-622