Description: Effect size is a quantitative measure that allows for the evaluation of the magnitude of a phenomenon, especially in the context of statistical research. Unlike p-values, which indicate the probability that observed results are due to chance, effect size provides a clearer perspective on the practical relevance of findings. It is expressed in various forms, such as mean differences, correlation, or odds ratios, and is essential for interpreting the importance of results in experimental and observational studies. A large effect size suggests that the intervention or phenomenon studied has a significant impact, while a small effect size may indicate that, although results are statistically significant, their practical relevance is limited. This measure is crucial in fields such as psychology, medicine, and social sciences, where the goal is not only to determine whether a relationship or effect exists but also to understand its magnitude and applicability in real-world situations. In summary, effect size is a vital tool for data interpretation, enabling researchers and professionals to make informed decisions based on the magnitude of observed effects.
History: The concept of effect size began to gain attention in the 1970s when researchers started to question the excessive reliance on p-values in interpreting statistical results. In 1977, Jacob Cohen published a seminal paper that laid the groundwork for the use of effect size in psychological and social research. Cohen introduced several measures of effect size, such as Cohen’s d and Pearson’s correlation, and emphasized the importance of reporting these values alongside conventional statistical results. Since then, effect size has evolved and been integrated into various disciplines, becoming a standard in the presentation of research findings.
Uses: Effect size is used in various research areas to assess the magnitude of observed effects in experimental and observational studies. In psychology, it is applied to measure the effectiveness of therapeutic interventions, while in medicine, it is used to evaluate the efficacy of treatments. In social sciences, it helps to understand the relationship between variables in correlation studies. Additionally, effect size is fundamental in meta-analyses, where results from multiple studies are combined to draw more robust conclusions.
Examples: An example of effect size is Cohen’s d, which is used to measure the difference between two means. If a study shows that a new drug reduces blood pressure by 10 mmHg compared to a placebo, and the calculated Cohen’s d is 0.8, this indicates a large effect size, suggesting that the drug has a significant impact. Another example is Pearson’s correlation, which can be used to assess the relationship between study time and academic performance, where a correlation coefficient of 0.5 would indicate a moderate relationship.
