In Effect Size we introduce the notion of effect size, and briefly mention Cohen’s d. We will now explain this concept further.
Definition 1: Cohen’s d, a statistic which is independent of the sample size and is defined as
where m_{1} and m_{2} represent two means and σ_{pooled} is some combined value for the standard deviation.
The effect size given by d is conventionally viewed as small, medium or large as follows:
- d = 0.20 – small effect
- d = 0.50 – medium effect
- d = 0.80 – large effect
For single sample hypothesis testing of the mean, we use the following value for d
Example 1: National norms for a school mathematics proficiency exam are distributed N(80,20). A random sample of 60 students from New York City is taken showing a mean proficiency score of 75 (as in Example 1 of Single Sample Hypothesis Testing). Find the effect size for the sample mean.
Per Definition 1,
which indicates a small effect. Note that the effect size is independent of the sample size. We should interpret d to mean that the sample mean is a quarter of a population standard deviation below the population mean.