Comparar métodos

	Amostragem Estratificada Desproporcional	Amostra Ponderada
Área	Metodologia de survey	Metodologia de survey
Família	Process / pipeline	Process / pipeline
Ano de origem	1934	1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)
Autor original	Jerzy Neyman	Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)
Tipo	Probability sampling design	Probability sampling design
Fonte seminal	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
Outros nomes	disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling	probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling
Relacionados	6	6
Resumo	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.	Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient.