Bootstrapping is a resampling technique used in statistics to estimate the sampling distribution of a statistic by generating multiple samples from the data. This method involves randomly sampling with replacement from the original dataset to create new samples of the same size. By repeatedly sampling from the data, researchers can estimate the variability of a statistic or parameter without making assumptions about the underlying distribution of the data. Bootstrapping can be used to calculate confidence intervals, test hypotheses, and assess the robustness of statistical methods. It is commonly used in fields such as finance, ecology, and machine learning.