There is a growing interest in the application of data science and information theory methods in reflectometry measurements; in particular for the improvement of experimental design and analysis of collected data . However, these recent works, in particular when Bayesian methods are applied, represent a resurgence of methods previously applied to reflectometry by Sivia and others in the 1990s . This early work involved the application of maximum entropy (MaxEnt) methods to develop free-form solutions for reflectometry data, the use of Bayesian model comparison to assess relative evidence for different analytical models, and utilisation of Bayesian parameter estimation. A component of this recent growth in popularity can be attributed to the inclusion of Bayesian inverse uncertainty estimation and parameter correlation quantification, through the use of Markov chain Monte Carlo methods, in popular reflectometry packages such as refnx, Refl1D, and RasCAL . However, to the authors' knowledge, there appears to be no explicit application of Bayesian model comparison methods to the determination of the best structural model for reflectometry analysis, beyond the example referenced above of Geoghagan et al.. Rather, the comprehensive consideration of model comparison is replaced with the application of a "rule-of-thumb", which states the most simple model that enables a description of the data should be used.