How to Calculate Skewness in Excel

In this Excel tutorial lesson, you will learn what skewness is and how to calculate skewness in an Excel application.

To find the skewness in Excel, please prepare some data tables.

Skewness data table

Calculate Skewness using SKEW function

Excel provides a SKEW function. Syntax is:

=SKEW(number1, [number2], …)

In this example, the formula is: =SKEW(B2:B13)

Skewness formula

Skewness here is strongly above 0 (skewness > 0), which means that it is a right-sided asymmetry. The conclusion is that for many months, sales were significantly lower than the average sales.

Calculate Skewness using Data Analysis ToolPak Add-In

First, you need to make sure your Data Analysis Toolpak Add-In is installed. Here is the lesson on how to install Data Analysis Toolpak Add-In.

Go to Ribbon to the Data tab. Click the Data Analysis button.

skewness Data Analysis ribbon button

A new window appears. Choose Descriptive Statistics and click OK.

skewness descriptive statistics

Enter the proper parameters:

  • Input Range – range of your data ($B$1:$B$13 in my example).
  • Labels in first row – there are labels in my data table.
  • Output Range – choose where you want to get your skewness calculated (I chose the same Sheet $E$1:$F$15).
  • Summary statistics – make sure it is checked.

skewness decriptive statistics parameters

And this is how you see my statistics summary. Skewness is calculated in row 10. The result is the same as in Example 1 where we checked how to calculate Skewness using the SKEW Excel function.

skewness calculated

Using the STDEV.P function and the AVG function

Another way to calculate skewness in Excel is to use the STDEV.P function and the AVG function. To do this, you need to calculate the standard deviation and the mean of the data first, and then use the following formula to calculate skewness:

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Skewness = 3 * (AVG – Mean) / STDEV.P

Where AVG represents the average of the data, Mean represents the mean of the data, and STDEV.P represents the standard deviation of the data.