scipy stats.normaltest() function | Python Last Updated : 11 Feb, 2019 Comments Improve Suggest changes Like Article Like Report 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: Python3 # 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: Python3 # 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) Comment More infoAdvertise with us Next Article scipy stats.normaltest() function | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads sciPy stats.nanstd() function | Python scipy.stats.nanstd(array, axis=0) function calculates the standard deviation by ignoring the Nan (not a number) values of the array elements along the specified axis of the array. It's formula - Parameters : array : Input array or object having the elements, including Nan values, to calculate the st 2 min read sciPy stats.mean() function | Python scipy.stats.mean(array, axis=0) function calculates the arithmetic mean of the array elements along the specified axis of the array (list in python). It's formula - Parameters : array: Input array or object having the elements to calculate the arithmetic mean. axis: Axis along which the mean is to b 1 min read scipy stats.kurtosistest() function | Python scipy.stats.kurtosistest(array, axis=0) function test whether the given data set has normal kurtosis (Fisher or Pearson) or not. What is Kurtosis ? It is the fourth central moment divided by the square of the variance. It is a measure of the "tailedness" i.e. descriptor of shape of probability distr 1 min read scipy stats.kurtosis() function | Python scipy.stats.kurtosis(array, axis=0, fisher=True, bias=True) function calculates the kurtosis (Fisher or Pearson) of a data set. It is the fourth central moment divided by the square of the variance. It is a measure of the "tailedness" i.e. descriptor of shape of probability distribution of a real-va 2 min read sciPy stats.sem() function | Python scipy.stats.sem(arr, axis=0, ddof=0) function is used to compute the standard error of the mean of the input data. Parameters : arr : [array_like]Input array or object having the elements to calculate the standard error. axis : Axis along which the mean is to be computed. By default axis = 0. ddof : 1 min read Like