Effect sizes are statistical measures that quantify the strength of a relationship or the magnitude of a difference between groups. They help researchers understand how significant their findings are, beyond just p-values. Common types of effect sizes include Cohen's d, which measures the difference between two means, and Pearson's r, which assesses the strength of a correlation between two variables. Effect sizes provide valuable context for interpreting research results. For example, a small effect size might indicate a minor difference that may not be practically important, while a large effect size suggests a more substantial impact. This information is crucial for making informed decisions in fields like psychology, medicine, and education.