scipy.stats.mstats.kurtosistest#

scipy.stats.mstats.kurtosistest(a, axis=0, alternative='two-sided')[source]#

Tests whether a dataset has normal kurtosis

Parameters:
aarray_like

array of the sample data

axisint or None, optional

Axis along which to compute test. Default is 0. If None, compute over the whole array a.

alternative{‘two-sided’, ‘less’, ‘greater’}, optional

Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):

  • ‘two-sided’: the kurtosis of the distribution underlying the sample is different from that of the normal distribution

  • ‘less’: the kurtosis of the distribution underlying the sample is less than that of the normal distribution

  • ‘greater’: the kurtosis of the distribution underlying the sample is greater than that of the normal distribution

New in version 1.7.0.

Returns:
statisticarray_like

The computed z-score for this test.

pvaluearray_like

The p-value for the hypothesis test

Notes

For more details about kurtosistest, see scipy.stats.kurtosistest.