In statistics, when we’re interested in determining whether or not there is a significant difference between two groups we often perform a hypothesis test, which results in a p-value. If this p-value is less than some significance level (common choices are 0.10, 0.05, and 0.01), we conclude that there is a statistically significant difference between the two groups.

However, while a p-value can tell us whether or not there is a statistically significant difference between, an effect size can tell us how large this difference actually is.

One of the most common measurements of effect size is **Cohen’s D**, which is calculated as:

Cohen’s D = (x_{1} – x_{2}) / pooled SD

where:

- x
_{1}= mean of group 1 - x
_{2}= mean of group 2 - pooled SD = √( [(n
_{1}-1)s_{1}^{2 }+ (n_{2}-1)s_{2}^{2}] / [n_{1}+ n_{2}– 2] )

This tutorial explains how to calculate Cohen’s D in Excel.

**Example: Cohen’s D in Excel**

Perform the following steps to calculate Cohen’s D in Excel.

**Step 1: Enter the data.**

First, we will enter the values for the mean, standard deviation, and sample size (n) for two groups.

**Step 2: Calculate the difference in means.**

Next, we will calculate the difference between the group means.

**Step 3: Calculate the pooled standard deviation.**

Next, we will calculate the pooled standard deviation.

**Step 4: Calculate Cohen’s D.**

Lastly, we will calculate Cohen’s D.

Cohen’s D turns out to be **0.2965** for this example.

**How to Interpret Cohen’s D**

As a rule of thumb, here is how to interpret Cohen’s D:

**0.2**= Small effect size**0.5**= Medium effect size**0.8**= Large effect size

In our example, an effect size of **0.2965 **would likely be considered a small effect size. This means that even if the difference between the two group means is statistically significantly different, the actual difference between the group means is trivial.