Võrdle meetodeid

	Kahekordne valik	Klastrite valim	Kihistatud juhuslik valim
Valdkond	Valimi moodustamine	Küsitlusmetoodika	Küsitlusmetoodika
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1938	Early-to-mid 20th century; canonical treatment 1953/1977	1977
Looja	Jerzy Neyman	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	William G. Cochran
Tüüp	Multi-phase sampling design	Probability sampling design	Probability-based survey sampling design
Algallikas	Neyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association, 33(201), 101–116.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Rööpnimetused	Two-Phase Sampling	cluster random sampling, area sampling, one-stage cluster sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Seotud	4	5	2
Kokkuvõte	Double Sampling (also called two-phase or multistage sampling) is a survey design in which a large preliminary sample is collected using inexpensive methods or partial information, then a smaller subsample is drawn from it and measured in detail. Pioneered by Jerzy Neyman in 1938, it is particularly useful when a cheap surrogate measurement is available but true measurement is expensive.	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.	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.