Effect size is a critical metric in statistical analysis that quantifies the magnitude of a relationship or difference between groups. In a statistical table, effect size is typically presented alongside other key metrics like p-values and confidence intervals to provide a comprehensive understanding of the results.

Common Representations in a Stats Table

1. Cohen's d: Used for t-tests, it measures the difference between two means relative to the pooled standard deviation. It is often displayed with benchmarks such as small (0.2), medium (0.5), and large (0.8) effects.

2. Pearson's r: For correlation analyses, it represents the strength and direction of a linear relationship between two variables. Values range from -1 to 1, with benchmarks like small (0.1), medium (0.3), and large (0.5).

3. Eta-Squared (η²): Common in ANOVA, it indicates the proportion of variance explained by a factor. Benchmarks include small (0.01), medium (0.06), and large (0.14).

4. Odds Ratios: Used in logistic regression, it quantifies the odds of an event occurring in one group compared to another. It is often accompanied by confidence intervals for interpretability.

5. R-Squared (R²): In regression models, it shows the proportion of variance in the dependent variable explained by the independent variables. Higher values indicate a better fit.