5.8.6.8. scipy.stats.normaltest¶

scipy.stats.normaltest(a, axis=0, nan_policy='propagate')[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 [R421], [R422] 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. nan_policy : {‘propagate’, ‘raise’, ‘omit’}, optional Defines how to handle when input contains nan. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’.

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

[R421]

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

[R422]

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