Ranks as a maximal invariant under increasing transformations

On samples of distinct real observations, the labeled rank vector is a maximal invariant for simultaneous strictly increasing bijections of the real line. Such transformations preserve every comparison. Conversely two samples with the same ranks can be matched by a piecewise linear increasing bijection through their corresponding ordered observations, with positive-slope tails. Under a common continuous probability distribution, every labeled ordering is equally likely by exchangeability, giving distribution-free rank tests. This argument does not assume that all continuous distributions lie in one orbit of real-line homeomorphisms.