Posterior Predictive Design is a Bayesian experimental design framework that selects experiments based on the predicted informativeness of future data, as evaluated through the posterior predictive distribution, thereby distinguishing itself from other design criteria by explicitly accounting for uncertainty in model parameters and the variability of potential future observations.