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Cohen’s d is a measure of effect size, which is used to compare the magnitude of the difference between two means. It is calculated by dividing the difference in means between two groups by the standard deviation of the pooled data from both groups. It can be interpreted as a measure of the amount of overlap between the two distributions, with a larger value indicating a greater difference. A Cohen’s d of 0.2 is considered a small effect size, 0.5 is considered a medium effect size, and 0.8 is considered a large effect size. For example, if Group A scored an average of 5 points on a test, and Group B scored an average of 8 points, and the standard deviation of the pooled data was 3 points, then the Cohen’s d would be 0.67, indicating a medium-sized effect.
In statistics, we often use to determine if there is a statistically significant difference between the mean of two groups.
However, while a p-value can tell us whether or not there is a statistically significant difference between two groups, 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 = (x1 – x2) / √(s12 + s22) / 2
where:
- x1 , x2: mean of sample 1 and sample 2, respectively
- s12, s22: variance of sample 1 and sample 2, respectively
Using this formula, here is how we interpret Cohen’s d:
- A d of 0.5 indicates that the two group means differ by 0.5 standard deviations.
- A d of 1 indicates that the group means differ by 1 standard deviation.
- A d of 2 indicates that the group means differ by 2 standard deviations.
And so on.
Here’s another way to interpret cohen’s d: An effect size of 0.5 means the value of the average person in group 1 is 0.5 standard deviations above the average person in group 2.
The following table shows the percentage of individuals in group 2 that would be below the average score of a person in group 1, based on cohen’s d.
| Cohen’s d | Percentage of Group 2 who would be below average person in Group 1 |
|---|---|
| 0.0 | 50% |
| 0.2 | 58% |
| 0.4 | 66% |
| 0.6 | 73% |
| 0.8 | 79% |
| 1.0 | 84% |
| 1.2 | 88% |
| 1.4 | 92% |
| 1.6 | 95% |
| 1.8 | 96% |
| 2.0 | 98% |
| 2.5 | 99% |
| 3.0 | 99.9% |
We often use the following rule of thumb when interpreting Cohen’s d:
- A value of 0.2 represents a small effect size.
- A value of 0.5 represents a medium effect size.
- A value of 0.8 represents a large effect size.
The following example shows how to interpret Cohen’s d in practice.
Example: Interpreting Cohen’s d
Suppose a botanist applies two different fertilizers to plants to determine if there is a significant difference in average plant growth (in inches) after one month.
Fertilizer #1:
- x1: 15.2
- s1: 4.4
Fertilizer #2:
- x2: 14
- s2: 3.6
Here is how we would calculate Cohen’s d to quantify the difference between the two group means:
- Cohen’s d = (x1 – x2) / √(s12 + s22) / 2
- Cohen’s d = (15.2 – 14) / √(4.42 + 3.62) / 2
- Cohen’s d = 0.2985
Cohen’s d is 0.2985.
Here’s how to interpret this value for Cohen’s d: The average height of plants that received fertilizer #1 is 0.2985 standard deviations greater than the average height of plants that received fertilizer #2.
Using the rule of thumb mentioned earlier, we would interpret this to be a small effect size.
In other words, whether or not there is a statistically significant difference in the mean plant growth between the two fertilizers, the actual difference between the group means is trivial.
The following tutorials offer additional information on effect size and Cohen’s d: