Kurtosis is a statistical measure that describes the shape of a distribution. It is defined as the degree of peakedness or flatness of a distribution relative to the normal distribution.

There are two types of kurtosis: excess kurtosis and normal kurtosis. Excess kurtosis is a measure of the amount of peakedness or flatness of a distribution relative to the normal distribution. Normal kurtosis is a measure of the peakedness or flatness of a distribution relative to the normal distribution.

An example of excess kurtosis is a distribution with a high degree of peakedness or sharpness. This would be a distribution where the data points are concentrated around the mean, with a small number of data points in the tails of the distribution. An example of this type of distribution would be the distribution of test scores for a class where most students scored near the average, but a few students scored very high or very low.

An example of normal kurtosis is a distribution with a moderate degree of peakedness or flatness. This would be a distribution where the data points are spread out evenly around the mean, with a relatively equal number of data points in the tails of the distribution. An example of this type of distribution would be the distribution of heights for a population of adults, where most people are of average height, but some people are taller or shorter than average.

In general, kurtosis is an important measure of the shape of a distribution because it can provide insights into the concentration of data points around the mean, as well as the spread of data points in the tails of the distribution. This information can be useful for understanding the underlying characteristics of a population or data set, and can be used to make predictions or inferences about the distribution.