Abstract

Abstract In sampling from a finite population, we often find it reasonable to posit a probability model (“superpopulation model”) that characterizes relations among variables that pertain to the units of the population. Such a model enables us to make inferences about population characteristics based on sample measurements and “auxiliary information”. In effect, we use the model to predict values for those units of the population that have not been sampled. In such prediction‐based (or model‐based) sampling and inference, it is important to guard against problems arising from model failure. Selecting a balanced sample or weighted balanced sample provides bias protection in the important case of estimating means or totals. Robust variance estimators guard against misapprehension of the variance structure.

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