Statistical Effect Sizes in Sports Science

Abstract

Understanding the impact of various interventions, training methods, and strategies is crucial in sports science. Statistical effect sizes are essential tools that quantify the magnitude of these effects, providing more insight than simple significance testing. This article explores the most commonly used effect size metrics in sports science, including Cohen's d, Hedges' g, Pearson's r, and Eta Squared (η²). By examining these metrics, we highlight their importance in assessing practical significance, comparing results across studies, and informing evidence-based practice. Furthermore, the article delves into the interpretation and application of these effect sizes, offering guidance on their use in research and practice to enhance the understanding and optimization of athletic performance and well-being. This comprehensive overview aims to equip sports scientists, coaches, and practitioners with the knowledge to apply these statistical tools effectively, ultimately improving the quality and impact of sports science research. Additionally, the article discusses the context-specific importance of these effect size measures, ensuring that readers can accurately interpret and utilize them in diverse research scenarios.