Porovnat metody

	Dvojí výběr	Stratifikovaný výběr	Systematický výběr
Obor	Výběr vzorku	Metodologie dotazníkových šetření	Metodologie dotazníkových šetření
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1938	1977	Mid-20th century (Cochran 1953; Kish 1965)
Tvůrce	Jerzy Neyman	William G. Cochran	William G. Cochran; formalized in survey sampling theory
Typ	Multi-phase sampling design	Probability-based survey sampling design	Probability sampling design
Původní zdroj	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: 978-0-471-16240-7	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Další názvy	Two-Phase Sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Příbuzné	4	2	5
Shrnutí	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.	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.