  Conformal prediction has been a very popular method of distribution-free
predictive inference in recent years in machine learning and statistics. Its
popularity stems from the fact that it works as a wrapper around any prediction
algorithm such as neural networks or random forests. Exchangeability is at the
core of the validity of conformal prediction. The concept of exchangeability is
also at the core of rank tests widely known in nonparametric statistics. In
this paper, we review the concept of exchangeability and discuss the
implications for conformal prediction and rank tests. We provide a low-level
introduction to these topics, and discuss the similarities between conformal
prediction and rank tests.
