Effect size is a quantitative measure of the magnitude of a phenomenon or the strength of a relationship in a statistical context. It provides a way to understand the practical significance of research findings beyond just p-values, which only indicate whether an effect exists.
There are several types of effect size measures, including:
Cohen's d: Used for comparing the means of two groups. It is calculated as the difference between the group means divided by the pooled standard deviation. Common benchmarks are:
- Small effect: 0.2
- Medium effect: 0.5
- Large effect: 0.8
Pearson's r: Measures the strength and direction of the linear relationship between two variables, ranging from -1 to 1.
Eta-squared (η²): Used in the context of ANOVA to measure the proportion of variance in the dependent variable that is attributable to a factor.
Odds ratio: Commonly used in case-control studies to compare the odds of an outcome occurring in one group versus another.
Effect size is important for interpreting the results of research, as it provides a clearer picture of the practical implications of findings, allowing for better comparisons across studies.
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