Statistical inference using bootstrap confidence intervals

Bootstrap confidence intervals provide a way of quantifying the uncertainties in the inferences that can be drawn from a sample of data. The idea is to use a simulation, based on the actual data, to estimate the likely extent of sampling error. Michael Wood explains how simple bootstrapping works and explores some of its advantages.