R-squared › When R-squared is Misleading

R-squared can be misleading in several scenarios. For nonlinear relationships, R-squared assumes linearity and might be low even when the model captures the true relationship well. Outliers can dramatically affect R-squared, making it unreliable for overall model performance. When variance changes across the prediction range (heteroscedasticity), R-squared might not reflect true model quality. Overfitting can make R-squared artificially high when the model has too many parameters relative to observations. Finally, with very small sample sizes, R-squared can be unstable and misleading.