The concept of effect size is crucial in interpreting the practical significance of statistical results. Effect size measures the magnitude or strength of the relationship or difference observed in a statistical analysis. It provides a standardized way to quantify the extent to which a particular phenomenon or intervention has an impact in the population.

In the context of biostatistics, effect size is often used to express the size of a treatment effect, the strength of an association between variables, or the magnitude of a difference between groups. Unlike statistical significance, which indicates whether an observed effect is likely due to chance, effect size focuses on the magnitude of the effect and is less influenced by sample size.

Commonly used effect size measures include:

1. Cohen’s d (for means comparison): It represents the standardized difference between two means and is calculated by dividing the difference between means by the pooled standard deviation.
2. Odds Ratio (for binary outcomes): It measures the odds of an event occurring in one group compared to another.
3. Correlation Coefficient (for association between variables): It quantifies the strength and direction of a linear relationship between two variables, often denoted by $r$.

A larger effect size indicates a more substantial or clinically relevant impact. Researchers and practitioners often consider both statistical significance and effect size when interpreting results. While a study may find a statistically significant result, the effect size helps determine whether the observed difference or association is practically meaningful.

In summary, understanding and reporting effect size alongside statistical significance contributes to a more comprehensive and meaningful interpretation of research findings in the context of biological and medical studies.