# How To Compute Skewness In Excel

Understanding the skewness of a dataset is crucial for statistical analysis, indicating the asymmetry of the data distribution. Excel provides functions and tools to calculate skewness, but the process can be complex and time-consuming.

This guide will walk you through the steps to compute skewness in Excel efficiently. We will also explore how Sourcetable offers a more streamlined approach for this task compared to Excel.

## Compute Skewness in Excel

### Using the SKEW Function

To calculate the skewness of a data distribution in Microsoft Excel, use the SKEW function. Skewness measures the asymmetry of the distribution around the mean. The function takes up to 255 arguments, with at least one number required.

### Function Syntax

The syntax for the SKEW function is =SKEW(number1, [number2], ...). The number1 argument is mandatory, while number2 and subsequent numbers are optional. Arguments can be numbers, names, arrays, or references that contain numbers.

### Arguments Accepted

The SKEW function processes logical values and text representations of numbers. Arrays or array references are valid inputs. However, the function will ignore text strings, logical values, and empty cells within array or reference arguments. Cells containing zero are included in the calculation.

### Error Conditions

Be aware that the SKEW function returns a #DIV/0! error if there are fewer than three data points or if the sample's standard deviation is zero, indicating no variability in the data.

### Steps to Compute Skewness

To compute skewness, input your data into a column or row, select an empty cell, and enter the SKEW function with the range of your data as the argument. Press Enter to view the skewness value, which indicates the direction and degree of asymmetry of your distribution.

## Common Use Cases

• Identifying the symmetry or asymmetry of data distribution in a dataset

• Assessing the normality of stock returns for financial analysis

• Evaluating the bias in a sample for statistical research

• Analyzing customer purchase behavior for retail sales forecasting

• Detecting outliers or anomalies in quality control data

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