Bootstrap is a computer-intensive technique of re-sampling the data to obtain the sampling distribution of a statistic, treating the initial sample as the population from which samples are drawn repeatedly and randomly, with replacement.
Bootstrap methods are resampling techniques that provide measures of accuracy like confidence intervals and standard errors without relying on parametric assumptions. These techniques are essential in statistical inference when the underlying distribution is unknown or complex.
