Skewness And Kurtosis Normality. D agostino s k squared test is a goodness of fit normality test based on a combination of the sample skewness and sample kurtosis as is the jarque bera test for normality. The null hypothesis for this test is that the variable is normally distributed.
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A normally distributed data has both skewness and kurtosis equal to zero. Just like skewness kurtosis is a moment based measure and it is a central standardized moment. Values outside that range may still be acceptable.
Now excess kurtosis will vary from 2 to infinity.
A distribution or data set is symmetric if it looks the same to the left and right of the center point. Now excess kurtosis will vary from 2 to infinity. Kurtosis is sensitive to departures from normality on the tails. Another way to test for normality is to use the skewness and kurtosis test which determines whether or not the skewness and kurtosis of a variable is consistent with the normal distribution.
