Сравнение методов

	Взвешенная стратифицированная выборка	Стратифицированная выборка с непропорциональным распределением
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	1953–1965	1934
Автор метода	Leslie Kish; William G. Cochran	Jerzy Neyman
Тип	Probability sampling with weighting	Probability sampling design
Основополагающий источник	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Другие названия	stratified sampling with weights, design-weighted stratified sampling, post-stratification weighting, WSS	disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling
Связанные	6	6
Сводка	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.	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.