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scipy stats.normaltest() function | Python

Last Updated : 11 Feb, 2019

scipy.stats.normaltest(array, axis=0) function test whether the sample is different from the normal distribution. This function tests the null hypothesis of the population that the sample was drawn from.

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:

# Performing normaltest 
from scipy.stats import normaltest 
import numpy as np  
import pylab as p  
  
x1 = np.linspace( -5, 5, 1000 ) 
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  ) 
  
p.plot(x1, y1, '.') 
  
print( '\nNormal test for given data :\n', normaltest(y1)) 

Output :



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

Code #2:

# Performing normaltest 
from scipy.stats import normaltest 
import numpy as np  
import pylab as p  
  
x1 = np.linspace( -5, 12, 1000 ) 
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  ) 
  
p.plot(x1, y1, '.') 
  
print( '\nNormal test for given data :\n', normaltest(y1)) 

Output :



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

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