resampling methods

Resampling methods are techniques used in statistics to assess the reliability of data analysis results by repeatedly drawing samples from the original data. Examples include bootstrapping, where many new datasets are created by randomly selecting data points with replacement, and cross-validation, which divides data into parts to test how well a model performs on unseen data. These methods help estimate the accuracy, stability, or variability of statistical models or predictions without needing new data, providing a way to evaluate how well findings might generalize beyond the original dataset.