# How does kurtosis affect the skewness of variable

## How does kurtosis and skewness affect the shape of a distribution?

A

**distribution**with a negative**kurtosis**value indicates that the**distribution**has lighter tails and a flatter peak than the normal**distribution**. For example, data that follow a beta**distribution**with first and second**shape**parameters equal to 2 have a negative**kurtosis**value.## What does skewness and kurtosis indicate?

**Skewness**is a measure of symmetry, or more precisely, the lack of symmetry.

**Kurtosis**is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high

**kurtosis**tend to have heavy tails, or outliers.

## How is skewness different from kurtosis?

**Skewness**is a measure of the degree of lopsidedness in the frequency distribution. Conversely,

**kurtosis**is a measure of degree of tailedness in the frequency distribution.

**Skewness**is an indicator of lack of symmetry, i.e. both left and right sides of the curve are unequal, with respect to the central point.

## How does kurtosis affect mean?

When a set of approximately normal data

**is**graphed via a histogram, it shows a bell peak and most data within + or – three standard deviations of the**mean**. However, when high**kurtosis is**present, the tails extend farther than the + or – three standard deviations of the normal bell-curved distribution.