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# scipy.stats.mstats.normaltest

`scipy.stats.mstats.``normaltest`(a, axis=0)

  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 [R191], [R192] 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 If None, the array is treated as a single data set, regardless of its shape. Otherwise, each 1-d array along axis axis is tested.
  Returns : | k2 : 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`. p-value : float or array A 2-sided chi squared probability for the hypothesis test.

  References

  [R191] | (1, 2) D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size,” Biometrika, 58, 341-348

  [R192] | (1, 2) D’Agostino, R. and Pearson, E. S. (1973), “Testing for departures from normality,” Biometrika, 60, 613-622

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