Effect Size
Effect size is a quantitative measure of the magnitude of a phenomenon or the strength of a relationship between variables in statistical analysis. It provides a standardized way to express the practical significance of research findings, independent of sample size, complementing p-values that only indicate statistical significance. Common types include Cohen's d, Pearson's r, and odds ratios, used across fields like psychology, medicine, and data science.
Developers should learn effect size when working with data analysis, A/B testing, or machine learning to interpret results beyond statistical significance, as it helps assess real-world impact and make informed decisions. For example, in A/B testing for software features, effect size quantifies user behavior changes, while in data science, it evaluates model performance or feature importance, ensuring findings are meaningful and actionable.