Importance Of Kurtosis In Statistics. Skewness and kurtosis are two commonly listed values when you run a software s descriptive statistics function. It tells us the extent to which the distribution is more or less outlier prone heavier or light tailed than the normal distribution.
Three different types of curves courtesy of investopedia are shown as follows. High kurtosis in a data set is an indicator that data has heavy tails or outliers. It is used to describe the extreme values in one versus the other tail.
A distribution that has tails shaped in roughly the same way as any normal distribution not just the standard normal distribution is said to be mesokurtic.
If there is a high kurtosis then we need to investigate why do we have so many outliers. Skewness essentially measures the symmetry of the distribution while kurtosis determines the heaviness of the distribution tails. It is difficult to discern different types of kurtosis from the density plots left panel because the tails are close to zero for all distributions. It is actually the measure of outliers present in the distribution.
