Bootstrap

Bootstrap is a powerful method that estimates statistical uncertainty by repeatedly drawing resamples from the available dataset. It is especially useful when theoretical distributional assumptions are weak and we still need confidence intervals or performance distributions. It plays an important role in model comparison and in assessing the stability of evaluation metrics. Although conceptually simple, it is highly flexible and practical. It works by relying directly on the data to understand uncertainty.