In statistics, **skewness **and **kurtosis **are two ways to measure the shape of a distribution.

**Skewness **is a measure of the asymmetry of a distribution. This value can be positive or negative.

- A negative skew indicates that the tail is on the left side of the distribution, which extends towards more negative values.
- A positive skew indicates that the tail is on the right side of the distribution, which extends towards more positive values.
- A value of zero indicates that there is no skewness in the distribution at all, meaning the distribution is perfectly symmetrical.

**Kurtosis **is a measure of whether or not a distribution is heavy-tailed or light-tailed relative to a normal distribution.

- The kurtosis of a normal distribution is 3.
- If a given distribution has a kurtosis less than 3, it is said to be
*playkurtic*, which means it tends to produce fewer and less extreme outliers than the normal distribution. - If a given distribution has a kurtosis greater than 3, it is said to be
*leptokurtic*, which means it tends to produce more outliers than the normal distribution.

This tutorial explains how to calculate both the skewness and kurtosis of a given dataset in Google Sheets.

**Example: Skewness & Kurtosis in Google Sheets**

Suppose we have the following dataset:

To calculate the skewness and kurtosis of this dataset, we can use the **SKEW()** and **KURT()** functions with the following syntax:

**SKEW(Array of values)****KURT(Array of values)**

It’s important to note that either function will return the error **#DIV/0!** in the following two scenarios:

- If there are fewer than three data points.
- If the sample standard deviation is zero.

The image below shows how to use these functions for our particular dataset:

The skewness turns out to be **-0.18490** and the kurtosis turns out to be **0.34624**.

**Additional Resource: Skewness & Kurtosis Calculator**

You can also calculate the skewness for a given dataset using the Statology Skewness and Kurtosis Calculator, which automatically calculates both the skewness and kurtosis for a given dataset.