Samplingus

Samplingus is a theoretical framework in statistics and data science for selecting samples from a population under uncertainty. It combines elements of stratified sampling with adaptive, data-driven weighting to improve representativeness and efficiency. The framework emphasizes explicit probabilistic models, transparent design decisions, and reproducible reporting of sampling probabilities and error estimates.

The term samplingus is a neologism formed from "sampling" and the Latin suffix -us, used in hypothetic or exploratory discussions of adaptive sampling methods. It is not part of established statistical nomenclature and has no single formal definition in mainstream practice; instead, it acts as a container concept for ideas about how to adapt sampling processes in the presence of incomplete information.

Core methodology: begin with a defined population and sampling frame; assign initial inclusion probabilities; collect an initial sample; update inclusion probabilities using Bayesian or pseudo-Bayesian updating based on observed data; reallocate future sampling effort to underrepresented strata or areas; iterate until stopping criteria are met (precision target, cost limit, or coverage requirements). Population estimates use weighted, design-based or model-based approaches, with weights derived from the samplingus process.

Applications and limitations: Samplingus is discussed mainly in theoretical or pedagogical contexts, with potential applications in public opinion polling, ecological surveys, and market research. Advantages include improved representativeness under constraint and potentially greater efficiency through adaptive allocation. Limitations include the need for explicit model assumptions, sensitivity to misspecification of priors, computational overhead, and challenges in maintaining transparency and auditability.

See also: sampling, adaptive sampling, stratified sampling, Bayesian statistics, survey design.