In the context of empirical risk minimization, how does increasing sample size affect generalization error while considering the bias-variance tradeoff?

When you train with more data, the empirical risk gets closer to the true risk, so the part of the error caused by over‑fitting—variance—drops, while the bias (the gap between the model’s best possible fit and the true function) stays unchanged. This means the overall generalization error usually falls because the variance term shrinks, but the bias term remains the same if you keep the same model class. For instance, if you fit a linear model to a noisy dataset, adding more observations will make the estimated coefficients more stable, reducing prediction noise, but the linear model’s inability to capture a nonlinear truth will still keep a fixed bias. Thus, larger sample sizes mainly improve generalization by lowering variance, helping the bias‑variance tradeoff tilt toward better overall accuracy.