What is the purpose of resampling? Why would we want to use it?

Resampling techniques?are a set of methods to either repeat sampling from a given?sample?or?population or a way to estimate the?precision?of a statistic. Although the process sounds complicated, the math involved is relatively simple and only requires a high school-level understanding of algebra. Informally,?resample?can mean something a little simpler: repeat?any?sampling method. A resample and test should be done whenever running a test doesn’t lead to a conclusion.

“Bootstrapping is a statistical procedure that resamples a single dataset to create many simulated samples. This process allows for the calculation of standard errors, confidence intervals, and hypothesis testing” (Jim Frost, https://statisticsbyjim.com/hypothesis-testing/bootstrapping/)

Cross-validation is a statistical method for validating a?predictive model. Subsets of the data are held out for use as validating sets; a model is fit to the remaining data (a training set) and used to predict the validation set. Averaging the quality of the predictions across the validation sets yields an overall measure of prediction accuracy. Cross-validation is employed repeatedly in building decision trees.

Resampling methods are part of my line of duties. Most of the time, I perform random sampling—from all the samples taken, given an equal probability of being selected. Then, to create a replicate of the collected samples, we must estimate the model parameters and the process must be repeated several times.