Comparer des méthodes

	Échantillonnage stratifié pondéré	Échantillonnage en grappes
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1953–1965	Early-to-mid 20th century; canonical treatment 1953/1977
Auteur d'origine	Leslie Kish; William G. Cochran	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Type	Probability sampling with weighting	Probability sampling design
Source fondatrice	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Alias	stratified sampling with weights, design-weighted stratified sampling, post-stratification weighting, WSS	cluster random sampling, area sampling, one-stage cluster sampling
Apparentées	6	5
Résumé	Weighted stratified sampling divides a population into non-overlapping strata and draws a probability sample from each stratum, then attaches a design weight to every selected unit so that estimates correctly represent the full population. Weights compensate for unequal selection probabilities that arise from disproportionate stratum allocations, non-response, or frame imperfections, making the procedure the backbone of most large-scale national and international surveys.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.