Effect Size

Effect size is the bridge between statistical significance and practical importance. A result may be statistically significant in terms of its p-value, but that does not necessarily mean the difference is meaningful in practice. Effect size tells us how large the difference actually is. This matters especially in large samples, where even tiny effects can appear significant. In machine learning and experiment design, effect size helps clarify the difference between “statistically present” and “meaningful for the business or application.”