Resampling is a statistical technique used to draw new samples from an existing dataset. This method helps estimate the properties of a population by creating multiple samples, which can provide insights into variability and uncertainty. Common resampling methods include bootstrapping and cross-validation, each serving different purposes in data analysis. In bootstrapping, random samples are taken with replacement from the original dataset, allowing for the estimation of confidence intervals and other statistics. Cross-validation, on the other hand, involves dividing the dataset into subsets to assess the performance of predictive models, ensuring they generalize well to new data.