Traditional sample survey theory largely focused on descriptive parameters, such as finite population means, totals and quantiles, from samples selected according to efficient probability sampling designs (subject to cost constraints). Associated inferences consist of point estimation, standard errors of estimators and large-sample confidence intervals based on normal approximations. Inferences, based on the known probability distribution induced by the sampling design with the population item values held fixed but unknown, are essentially non-parametric.