scipy.stats.skewtest

scipy.stats.skewtest(a, axis=0, nan_policy='propagate')

Test whether the skew is different from the normal distribution.

This function tests the null hypothesis that the skewness of the population that the sample was drawn from is the same as that of a corresponding normal distribution.

Parameters:

a : array

The data to be tested

axis : int or None, optional

Axis along which statistics are calculated. 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

The computed z-score for this test.

pvalue : float

a 2-sided p-value for the hypothesis test

Notes

The sample size must be at least 8.

References

[1]	R. B. D’Agostino, A. J. Belanger and R. B. D’Agostino Jr., “A suggestion for using powerful and informative tests of normality”, American Statistician 44, pp. 316-321, 1990.

Examples

>>> from scipy.stats import skewtest
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8])
SkewtestResult(statistic=1.0108048609177787, pvalue=0.3121098361421897)
>>> skewtest([2, 8, 0, 4, 1, 9, 9, 0])
SkewtestResult(statistic=0.44626385374196975, pvalue=0.6554066631275459)
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8000])
SkewtestResult(statistic=3.571773510360407, pvalue=0.0003545719905823133)
>>> skewtest([100, 100, 100, 100, 100, 100, 100, 101])
SkewtestResult(statistic=3.5717766638478072, pvalue=0.000354567720281634)