SciPy

scipy.stats.normaltest

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 [R251], [R252] 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

If None, the array is treated as a single data set, regardless of its shape. Otherwise, each 1-d array along axis axis is tested.

Returns :

k2 : 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.

p-value : float or array

A 2-sided chi squared probability for the hypothesis test.

References

[R251](1, 2) D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size,” Biometrika, 58, 341-348
[R252](1, 2) D’Agostino, R. and Pearson, E. S. (1973), “Testing for departures from normality,” Biometrika, 60, 613-622

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