`scipy.stats._stats_py:skewtest`

source: /scipy/stats/_stats_py.py :1595

Signature

   def     skewtest (    a    ,    axis    =  0   ,    nan_policy    =  propagate   ,    alternative    =  two-sided   ,  * ,     keepdims    =  False     )

Summary

Test whether the skew is different from the normal distribution.

Extended Summary

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. Must contain at least eight observations.

axis : int or None, default: 0

If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If None, the input will be raveled before computing the statistic.

nan_policy : {'propagate', 'omit', 'raise'}

Defines how to handle input NaNs.

propagate: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN.
omit: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN.
raise: if a NaN is present, a ValueError will be raised.

alternative : {'two-sided', 'less', 'greater'}, optional

Defines the alternative hypothesis. Default is 'two-sided'. The following options are available:

'two-sided': the skewness of the distribution underlying the sample is different from that of the normal distribution (i.e. 0)
'less': the skewness of the distribution underlying the sample is less than that of the normal distribution
'greater': the skewness of the distribution underlying the sample is greater than that of the normal distribution

keepdims : bool, default: False

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Returns

statistic : float: The computed z-score for this test.
pvalue : float: The p-value for the hypothesis test.

Notes

The sample size must be at least 8.

Beginning in SciPy 1.9, np.matrix inputs (not recommended for new code) are converted to np.ndarray before the calculation is performed. In this case, the output will be a scalar or np.ndarray of appropriate shape rather than a 2D np.matrix. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or np.ndarray rather than a masked array with mask=False.

Array API Standard Support

skewtest has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ✅                   
JAX                   ✅                     ✅                   
Dask                  ✅                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

from scipy.stats import skewtest

✓

skewtest([1, 2, 3, 4, 5, 6, 7, 8])
skewtest([2, 8, 0, 4, 1, 9, 9, 0])
skewtest([1, 2, 3, 4, 5, 6, 7, 8000])
skewtest([100, 100, 100, 100, 100, 100, 100, 101])
skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='less')
skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='greater')

✗

For a more detailed example, see :ref:`hypothesis_skewtest`.

Aliases

scipy.stats.skewtest