Comparer des méthodes

	Échantillonnage adaptatif multi-stades	Échantillonnage systématique
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1977 (multistage base); 1990-1992 (adaptive extensions by Thompson)	Mid-20th century (Cochran 1953; Kish 1965)
Auteur d'origine	Steven K. Thompson (adaptive principles); William G. Cochran (multistage framework)	William G. Cochran; formalized in survey sampling theory
Type	Probability-based adaptive sampling design	Probability sampling design
Source fondatrice	Thompson, S. K. (1992). Sampling. Wiley. ISBN: 978-0471548850	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Alias	AMS, adaptive multi-phase sampling, sequential multistage sampling, adaptive hierarchical sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Apparentées	5	5
Résumé	Adaptive multistage sampling combines the hierarchical efficiency of multistage designs with adaptive decision rules that adjust which units are sampled at later stages based on what is observed at earlier stages. It is used when a target characteristic is rare, clustered, or spatially heterogeneous and a fixed design would waste resources on uninformative areas of the population.	Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.