High complexity models in machine learning can sometimes overfit, meaning they fit the data too closely and don't generalize well. To address this, a new hypothesis test has been introduced to detect overfitting and evaluate model performance using training data. This test relies on concentration bounds to compare empirical means and true means, helping to identify overfitting and potential distributional shifts. This approach offers a quantitative way to define and detect overfitting, improving model generalization.