Skewness And Kurtosis. Kurtosis is descriptive or summary statistics and describes peakedness and frequency of extreme values in a distribution. The histogram can give you a general idea of the shape but two numerical measures of shape give a more precise evaluation.
A distribution or data set is symmetric if it looks the same to the left and right of the center point. So we can conclude from the above discussions that the horizontal push or pull distortion of a normal distribution curve gets captured by the skewness measure and the vertical push or pull distortion gets captured by the kurtosis measure. Skewness is a measure of symmetry or more precisely the lack of symmetry.
It measures the lack of symmetry in data distribution.
Skewness is a measure of the degree of lopsidedness in the frequency distribution. It is the degree of distortion from the symmetrical bell curve or the normal distribution. Conversely kurtosis is a measure of degree of tailedness in the frequency distribution. Statistically two numerical measures of shape skewness and excess kurtosis can be used to test for normality.
