Kurtosis measures how much data is in the tails of a distribution.

A distribution with large kurtosis will have "heavy tails", which means that there are a lot of outliers. A distribution with low Kurtosis is considered more "central", and will have fewer outliers.

## When To Use Kurtosis

Kurtosis is useful to measure when you want to gauge how many outliers a distribution might have. It can be used with a histogram to visualize the distribution. Skewness can be used to measure the symmetry of the distribution. A boxplot can be used to visualize the distribution's five-number summary.