Bootstrapping is a test that uses random sampling with replacement, and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. here’s how it works. firstly we start with a sample of data, Bootstrapping involves creating many “bootstrap samples” by randomly selecting data points from your original sample with replacement. This means that some data points may be selected multiple times in one bootstrap sample, while others may not be selected at all. For each of these bootstrap samples, we compute the statistic of interest. This could be the mean, median, standard deviation etc.With the collection of statistics obtained from the bootstrap samples, we can perform various analyses. For example, we can calculate the mean and standard error of the statistics, create confidence intervals, or conduct hypothesis tests.