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.