In this article, we will learn about bootstrap sampling, what it is, how it works and we will implement it sampling using Python

Bootstrap sampling is a general tool for assessing statistical accuracy. Rather than repeatedly obtaining independent data sets from the population to estimate a model parameters, the bootstrap method which is the resampling technique makes is possible to create new different samples from the original training.

Suppose we have a model of which we want to fit to a training set denoted by \(Z\). Let assume the parameter of interest is the standard deviation denoted \(\sigma\) and let \(\hat \sigma\) represent the sample statistic used to estimate \(\sigma\). Using bootstrap sampling the idea is to randomly sample with replacement from \(Z\), k new samples, each of which has the same size as the original training set. Since we are sampling k new samples this will produce k bootstrap datasets