Another class of methods assesses whether the inference was successful in light of the given observed data, for example, by comparing the posterior predictive distribution of summary statistics to the summary statistics observed. Beyond that, cross-validation techniques and predictive checks represent promising future strategies to evaluate the stability and out-of-sample predictive validity of ABC inferences. This is particularly important when modeling large data sets, because then the posterior support of a particular model can appear overwhelmingly conclusive, even if all proposed models in fact are poor representations of the stochastic system underlying the observation data. Out-of-sample predictive checks can reveal potential systematic biases within a model and provide clues on to how to improve its structure or parametrization.