In this course, we will rely on a method called the Bootstrap to approximate the sampling distribution of our statistics, insted of relying so directly on the Central Limit Theorem. The name bootstrap shows up a lot these days, and I’m positive you have used this word to describe something different than what we’ll talk about here. Our Bootstrap has nothing to do with compilers nor CSS libraries.
After estimating population parameters, a natural next question is, how certain are we in our estimate? By approximating sampling distributions, the (statistical) Bootstrap will be our primary means of quantifying uncertainty in our estimates. Such quantifications will primarily come in the form of confidence intervals.