Model Flexibility Analysis does not measure the persuasiveness of a fit

Recently, Veksler, Myers, and Gluck (2015) proposed model flexibility analysis as a method that “aidsmodel evaluation by providing a metric for gauging the persuasiveness of a given fit” (p. 755) Modelflexibility analysis measures the complexity of a model in terms of the proportion of all possible datapatterns it can predict. We show that this measure does not provide a reliable way to gauge complexity,which prevents model flexibility analysis from fulfilling either of the 2 aims outlined by Veksler et al.(2015): absolute and relative model evaluation. We also show that model flexibility analysis can even failto correctly quantify complexity in the most clear cut case, with nested models. We advocate for the useof well-established techniques with these characteristics, such as Bayes factors, normalized maximumlikelihood, or cross-validation, and against the use of model flexibility analysis. In the discussion, weexplore 2 issues relevant to the area of model evaluation: the completeness of current model selectionmethods and the philosophical debate of absolute versus relative model evaluation