# scipy stats.normaltest () function | python

` scipy.stats.normaltest (array, axis = 0) ` checks if the sample differs from the normal distribution. This function tests the null hypothesis of the population from which the sample was taken.

Parameters:
array: Input array or object having the elements.
axis: Axis along which the normal distribution test is to be computed. By default axis = 0.

Returns: k2 value and P-value for the hypothesis test on data set.

Code # 1:

 ` # Running a normal test ` ` from ` ` scipy.stats ` ` import ` ` normaltest ` ` import ` ` numpy as np ` ` import ` ` pylab as p ` ` `  ` x1 ` ` = ` ` np.linspace (` ` - ` ` 5 ` `, ` ` 5 ` `, ` ` 1000 ` `) ` ` y1 ` ` = ` ` 1. ` ` / ` ` (sqrt-in-python/">np. sqrt (` ` 2. ` ` * ` ` np.pi)) ` ` * ` ` np.exp (` ` - ` `. ` ` 5 ` ` * ` ` (x1) ` ` * ` ` * ` ` 2 ` `) `   ` p.plot (x1, y1, ` ` ’.’ ` `) `   ` print ` ` (` ` ’Normal test for given data:’ ` `, normaltest (y1)) `

Output:

`     Normal test for given data: NormaltestResult ( statistic = 146.08066794511544, pvalue = 1.901016994532079e-32) `

Code # 2:

 ` # Execute normal test ` ` from ` ` scipy.stats ` ` import ` ` normaltest ` ` import numpy as np `` import pylab as p    x1 = np.linspace ( - 5 , 12 , 1000 ) y1 = 1. / (sqrt-in-python/">np. sqrt ( 2. * np.pi)) * np.exp ( - . 5 * (x1) * * 2  )    p.plot (x1, y1, ’.’ )   print ( ’Normal test for given data:’ , normaltest (y1)) `

Output:

`     Normal test for given data: NormaltestResult (statistic = 344.05533061429884, pvalue = 1.9468577593501764e-75) `

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