Most of the statistical curricula, mainly that written at the elementary level, is based on the classical (frequentist) approach. The Bayesian school, even if originated in the 18th century, has only recently seen a strong development of its tools. This development, however, has not been seen in a basic level. The discipline, as well as the teachers, reflect the classical dominance, which reinforces the current paradigm. Although they have different starting points, both approaches, classical and Bayesian, have tools to analyze data, and we should offer the choice to the student. This article deals with two important concepts, one very useful from the classical point of view, which is the concept of independence, and the other related to the Bayesian thought, the concept of exchangeability. Definitions and simple examples are presented to relate both approaches, from an elementary point of view.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education