be both subjective and fallible. In response to these concerns, Neyman (1934) developed a design-based approach that introduced randomness via a set of known selection probabilities. This allowed for the selection process to be both controlled and known, avoiding untenable distributional assumptions. The selection probabilities were then used when fitting models to weight the sample in accordance with the characteristics of the population (e.g., Pfeffermann, 1993).
