Befitting Bootstrap Analysis: The Effect of Imbalance

Bootstrapping is a statistical inference method based on resampling a data set, with replacement. Befitting bootstrap analysis is a method of bootstrapping data that accounts for the data generation process. In this paper, we examine with case studies the effect of BBA in unbalanced data where the data generation is producing groups of unequal size. We provide a sensitivity analysis of bootstrapping and befitting bootstrap analysis and show how befitting bootstrap analysis is affected by imbalance, large or small. The Python code used in the analysis is available in a GitHub repository. The first section is an introduction. Section 2 is a methodological background on bootstrap analysis. Section 3 reviews befitting bootstrap analysis and befitting cross validation used in predictive analytics. In Section 4 there are two case studies based on a simulation and a dental adhesive strength study. Section 5 concludes the paper with a discussion and lessons learned from the case studies.