# Pearson’s Coefficient of Skewness in Excel (Step-by-Step)

Developed by biostatistician Karl Pearson, Pearson’s coefficient of skewness is a way to measure the skewness in a sample dataset.

There are actually two methods that can be used to calculate Pearson’s coefficient of skewness:

Method 1: Using the Mode

Skewness = (Mean – Mode) / Sample standard deviation

Method 2: Using the Median

Skewness = 3(Mean – Median) / Sample standard deviation

In general, the second method is preferred because the mode is not always a good indication of where the “central” value of a dataset lies and there can be more than one mode in a given dataset.

The following step-by-step example shows how to calculate both versions of the Pearson’s coefficient of skewness for a given dataset in Excel.

### Step 1: Create the Dataset

First, let’s create the following dataset in Excel: ### Step 2: Calculate the Pearson Coefficient of Skewness (Using the Mode)

Next, we can use the following formula to calculate the Pearson Coefficient of Skewness using the mode: The skewness turns out to be 1.295.

### Step 3: Calculate the Pearson Coefficient of Skewness (Using the Median)

We can also use the following formula to calculate the Pearson Coefficient of Skewness using the median: The skewness turns out to be 0.569.

### How to Interpret Skewness

We interpret the Pearson coefficient of skewness in the following ways:

• A value of 0 indicates no skewness. If we created a histogram to visualize the distribution of values in a dataset, it would be perfectly symmetrical.
• A positive value indicates positive skew or “right” skew. A histogram would reveal a “tail” on the right side of the distribution.
• A negative value indicates a negative skew or “left” skew. A histogram would reveal a “tail” on the left side of the distribution.

In our previous example, the skewness was positive which indicates that the distribution of data values was positively skewed or “right” skewed.