R², or R-squared, is a statistical metric widely used in regression analysis to evaluate the performance of a model. It represents 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 crucial for effective data analysis and model evaluation.