In
statistics, an
effect size is a quantitative measure of the strength of a phenomenon. Examples of effect sizes are the
correlation between two variables, the regression coefficient in a regression, the mean difference, or even the risk with which something happens, such as how many people survive after a heart attack for every one person that does not survive. For each type of effect-size, a larger
absolute value always indicates a stronger effect. Effect sizes complement
statistical hypothesis testing, and play an important role in
power analyses, sample size planning, and in
meta-analyses. They are the first item (magnitude) in
the MAGIC criteria for evaluating the strength of a statistical claim.