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# scipy.stats.normaltest

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

  Tests whether a sample differs from a normal distribution.

  This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D’Agostino and Pearson’s [R614], [R615] test that combines skew and kurtosis to produce an omnibus test of normality.

  Parameters: | a : array_like The array containing the data to be tested. axis : int 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. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’.
  Returns: | statistic : float or array `s^2 + k^2`, where `s` is the z-score returned by `skewtest` and `k` is the z-score returned by `kurtosistest`. pvalue : float or array A 2-sided chi squared probability for the hypothesis test.

  References

  [R614] | (1, 2) D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size”, Biometrika, 58, 341-348

  [R615] | (1, 2) D’Agostino, R. and Pearson, E. S. (1973), “Tests for departure from normality”, Biometrika, 60, 613-622