As useful as it may be to use the scientific method coupled with the classic seven and other quality tools, a root cause investigator’s toolbox would be incomplete without a method for forming the first tentative hypotheses. Exploratory data analysis (EDA) provides such a methodology. The concept of EDA was created by John Tukey for using statistical methods for hypothesis generation and searching through data for clues. Trip and de Mast (2007) believe “the purpose of EDA is the identification of dependent (Y-) and independent (X-) variables that may prove to be of interest for understanding or solving the problem under study” and EDA can “display the data such that their distribution is revealed” (p. 301).