R², or R-squared, is a statistical metric commonly used to evaluate the performance of regression models. It quantifies the proportion of variance in the dependent variable that can be explained by the independent variables in the model. R² values range from 0 to 1, where 0 indicates that the model does not explain any variance, and 1 signifies that it perfectly explains the variance. Despite its popularity, R² can be misunderstood, particularly regarding its implications for model accuracy and predictive power. Understanding R² is essential for effective data analysis and model evaluation in various fields, including statistics and machine learning.