Comparer des méthodes

	Échantillonnage stratifié disproportionné	Échantillonnage stratifié
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1934	1977
Auteur d'origine	Jerzy Neyman	William G. Cochran
Type	Probability sampling design	Probability-based survey 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: 978-0-471-16240-7
Alias	disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Apparentées	6	2
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.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.