Skewness And Kurtosis Of Normal Distribution. Values outside that range may still be acceptable. Kurtosis is a measure of how differently shaped are the tails of a distribution as compared to the tails of the normal distribution.
While skewness focuses on the overall shape kurtosis focuses on the tail shape. Skewness is a measure of symmetry or more precisely the lack of symmetry. The kurtosis can be even more convoluted.
Now let s look at the definitions of these numerical measures.
As the kurtosis measure for a normal distribution is 3 we can calculate excess kurtosis by keeping reference zero for normal distribution. If the peak of the distributed data was right of the average value that would mean a negative skew. Kurtosis is the fourth standardized central moment of the random variable of the probability distribution. If skewness is not close to zero then your data set is not normally distributed.
