# scipy.stats.skewtest

scipy.stats.skewtest(a, axis=0, nan_policy='propagate', alternative='two-sided')

  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. 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. 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

      New in version 1.7.0.

  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.

  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)
      >>> skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='less')
      SkewtestResult(statistic=1.0108048609177787, pvalue=0.8439450819289052)
      >>> skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='greater')
      SkewtestResult(statistic=1.0108048609177787, pvalue=0.15605491807109484)

scipy.stats.mood scipy.stats.kurtosistest