This is a statistical technique used to estimate the distribution of a statistic (like the mean or standard deviation) by randomly sampling with replacement from the data set. In this method, numerous samples are taken, each sample is analyzed, and the results are used to form a distribution. This approach is particularly useful when the original dataset is small, as it allows for the estimation of the distribution of a statistic without the need for a large number of original observations. Bootstrap resampling is commonly used in machine learning and data analysis to assess the uncertainty of a model or to improve model accuracy.