Võrdle meetodeid

	Klastrite valim	Kihistatud juhuslik valim	Süstemaatiline valim
Valdkond	Küsitlusmetoodika	Küsitlusmetoodika	Küsitlusmetoodika
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	Early-to-mid 20th century; canonical treatment 1953/1977	1977	Mid-20th century (Cochran 1953; Kish 1965)
Looja	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	William G. Cochran	William G. Cochran; formalized in survey sampling theory
Tüüp	Probability sampling design	Probability-based survey sampling design	Probability sampling design
Algallikas	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	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Rööpnimetused	cluster random sampling, area sampling, one-stage cluster sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Seotud	5	2	5
Kokkuvõte	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.	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.