Explain the term "effect size."

Effect size refers to a quantitative measure that indicates the magnitude of an effect or the strength of a relationship between variables in a study. It provides insight into how much of a change or difference is observed as a result of a particular treatment or intervention, which is crucial for understanding the practical significance of research findings.

In essence, effect size helps researchers and practitioners gauge whether an intervention is not only statistically significant but also practically relevant. This is important because statistical significance alone does not convey the size or importance of an effect; a small effect might be statistically significant in a large sample but may hold little real-world relevance.

Additionally, effect size can vary based on the context and type of data being analyzed, and is often presented in various forms such as Cohen's d, Pearson's r, or odds ratios, depending on the statistical analysis performed. These standardized measures allow for comparisons across different studies and interventions.

Understanding effect size is vital for evidence-based practice, as it helps in the evaluation of interventions and guides clinical decision-making by providing a clearer picture of the expected outcomes.