Kurtosis, like skewness, is used to describe the distribution of a dataset. Skewness considers which side of the distribution is weighted heavier than the other, Kurtosis on the other hand considers how heavily weighted the tails are relative to a normal distribution (N.B. A normal distribution has a Kurtosis of 3, hence the 3x used in Pearson’s 2nd coefficient formula).

The higher the Kurtosis of a distribution, the more likely outlying values are as the distribution is deemed to be “fat-tailed”. On the other hand a low kurtosis (less than 3), will lead to a tighter distribution.