# Can Kurtosis Be Negative?

In statistics, kurtosis is used to describe the shape of a probability distribution.

Specifically, it tells us the degree to which data values cluster in the tails or the peak of a distribution.

The kurtosis for a distribution can be negative, equal to zero, or positive.

## Zero Kurtosis

If a distribution has a kurtosis of 0, then it is equal to the normal distribution which has the following bell-shape: ## Positive Kurtosis

If a distribution has positive kurtosis, it is said to be leptokurtic, which means that it has a sharper peak and heavier tails compared to a normal distribution. This simply means that fewer data values are located near the mean and more data values are located on the tails.

The most well-known distribution that has a positive kurtosis is the t distribution, which has a sharper peak and heaver tails compared to the normal distribution.

## Negative Kurtosis

If a distribution has negative kurtosis, it is said to be platykurtic, which means that it has a flatter peak and thinner tails compared to a normal distribution. This simply means that more data values are located near the mean and less data values are located on the tails.

One extreme example of a distribution that has a negative kurtosis is the uniform distribution, which has no peak at all and is a completely flat distribution.

## When to Use Kurtosis in Practice

In practice, we often measure the kurtosis of a distribution in the exploratory phase of analysis when we’re just trying to get a better understanding of the data.

So, if we see that the kurtosis is positive then we know we’re working with a distribution that has fewer data values located near the center and more data values that are spread out along the tails.

Conversely, if we see that the kurtosis is negative then we know we’re working with a distribution that has more data values located near the center and less data values in the tails.