Resampling methods are statistical techniques used to assess the variability of a dataset by repeatedly drawing samples from it. These methods help estimate the distribution of a statistic, such as the mean or variance, without making strong assumptions about the underlying population. Common resampling techniques include bootstrapping and cross-validation. Bootstrapping involves taking multiple random samples with replacement from the original dataset to create a distribution of the statistic of interest. In contrast, cross-validation is often used in machine learning to evaluate a model's performance by dividing the data into training and testing sets multiple times, ensuring that the model is robust and generalizes well to new data.