Comparer des méthodes

	Échantillonnage stratifié disproportionné	Échantillonnage en grappes
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1934	Early-to-mid 20th century; canonical treatment 1953/1977
Auteur d'origine	Jerzy Neyman	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Type	Probability sampling design	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	disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling	cluster random sampling, area sampling, one-stage cluster sampling
Apparentées	6	5
Résumé	Disproportional stratified sampling divides the population into mutually exclusive strata and deliberately draws different proportions from each stratum — oversampling small or analytically important subgroups and undersampling large ones. Post-hoc weighting restores population-level representativeness when overall estimates are needed. First formalised by Jerzy Neyman in 1934, it is the standard approach when subgroup-level precision matters as much as total-population estimates.	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.