scipy.stats.kurtosistest#
- scipy.stats.kurtosistest(a, axis=0, nan_policy='propagate', alternative='two-sided')[source]#
Test whether a dataset has normal kurtosis.
This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution.
- Parameters
- aarray
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.
- nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional
Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):
‘propagate’: returns nan
‘raise’: throws an error
‘omit’: performs the calculations ignoring nan values
- 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
- statisticfloat
The computed z-score for this test.
- pvaluefloat
The p-value for the hypothesis test.
Notes
Valid only for n>20. This function uses the method described in [1].
References
- 1
see e.g. F. J. Anscombe, W. J. Glynn, “Distribution of the kurtosis statistic b2 for normal samples”, Biometrika, vol. 70, pp. 227-234, 1983.
Examples
>>> from scipy.stats import kurtosistest >>> kurtosistest(list(range(20))) KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.08804338332528348) >>> kurtosistest(list(range(20)), alternative='less') KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.04402169166264174) >>> kurtosistest(list(range(20)), alternative='greater') KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.9559783083373583)
>>> rng = np.random.default_rng() >>> s = rng.normal(0, 1, 1000) >>> kurtosistest(s) KurtosistestResult(statistic=-1.475047944490622, pvalue=0.14019965402996987)