scipy stats.skewtest() function | Python Last Updated : 11 Feb, 2019 Summarize Comments Improve Suggest changes Share Like Article Like Report scipy.stats.skewtest(array, axis=0) function test whether the skew is different from the normal distribution. This function tests the null hypothesis that the skewness of the population that the sample was drawn from is the same as that of a corresponding normal distribution. Its formula - Parameters : array : Input array or object having the elements. axis : Axis along which the skewness test is to be computed. By default axis = 0. Returns : Z-score (Statistics value) and P-value for the hypothesis test on data set. Code #1: Python3 # Performing skewtest from scipy.stats import skewtest 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( '\nSkewness test for given data :\n', skewtest(y1)) Output : Skewness test for given data : SkewtestResult(statistic=11.874007880556805, pvalue=1.6153913086650964e-32) Code #2: Python3 # Performing skewtest from scipy.stats import skewtest 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( '\nSkewness for data :\n', skewtest(y1)) Output : Skewness for data : SkewtestResult(statistic=16.957642860709516, pvalue=1.689888374767126e-64) Comment More infoAdvertise with us Next Article scipy stats.skew() | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads 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 sciPy stats.tsem() function | Python scipy.stats.tsem(array, limits=None, inclusive=(True, True)) calculates the trimmed standard error of the mean of array elements along the specified axis of the array. Its formula :- Parameters : array: Input array or object having the elements to calculate the trimmed standard error of the mean. ax 2 min read sciPy stats.tstd() function | Python scipy.stats.tstd(array, limits=None, inclusive=(True, True)) calculates the trimmed standard deviation of the array elements along the specified axis of the array. It's formula - Parameters : array: Input array or object having the elements to calculate the trimmed standard deviation. axis: Axis alo 2 min read sciPy stats.signaltonoise() function | Python scipy.stats.signaltonoise(arr, axis=0, ddof=0) function computes the signal-to-noise ratio of the input data. Its formula : Parameters :arr : [array_like]Input array or object having the elements to calculate the signal-to-noise ratio axis : Axis along which the mean is to be computed. By default ax 2 min read scipy stats.skew() | Python scipy.stats.skew(array, axis=0, bias=True) function calculates the skewness of the data set. skewness = 0 : normally distributed. skewness > 0 : more weight in the left tail of the distribution. skewness < 0 : more weight in the right tail of the distribution. Its formula - Parameters : array 2 min read sciPy stats.describe() function | Python scipy.stats.describe() function compute a variety of descriptive statistics for a dataset, including count, min/max, mean, variance, skewness and kurtosis. It's a convenient one-stop summary tool for numerical data analysis.Example:Pythonfrom scipy.stats import describe import numpy as np d = [4, 8, 2 min read Like