Bootstrapping is a method that involves repeatedly taking random samples from a set of data and using them to estimate properties of the entire dataset. This technique helps in understanding sample statistics, calculating standard errors, and determining confidence intervals for population parameters. The study discusses different types of bootstrapping methods, including univariate, multivariate, and residual bootstrapping, with examples provided. Additionally, the Jackknifing method is briefly mentioned as another resampling procedure.