Resampling is a statistical technique used to draw repeated samples from a dataset to assess variability or improve estimates. It helps in understanding the distribution of a statistic by creating multiple simulated samples, which can provide insights into the reliability of the results. Common methods of resampling include bootstrapping and cross-validation. Bootstrapping involves repeatedly sampling with replacement from the original data, while cross-validation is used to evaluate the performance of predictive models by partitioning the data into training and testing sets. Both methods enhance the robustness of statistical analyses.