Does More Data Always Yield Better Performance?

The conventional wisdom that more data always improves model performance is challenged through experimentation. Adding more samples can cause performance to fluctuate erratically due to biased data or spurious patterns, while adding more features can introduce noise and lead to overfitting. Experiments using a student dropout dataset demonstrate that both adding rows and columns can sometimes hurt model performance. Ultimately, the quality, consistency, and usefulness of the data are more critical for generalization than sheer quantity.