Порівняння методів

	Подвійна вибірка	Вибірка з ранжованих наборів	Систематичний відбір
Галузь	Вибірка	Вибірка	Методологія опитувань
Родина	Process / pipeline	Process / pipeline	Process / pipeline
Рік появи	1938	1952	Mid-20th century (Cochran 1953; Kish 1965)
Автор методу	Jerzy Neyman	Glenn A. McIntyre	William G. Cochran; formalized in survey sampling theory
Тип	Multi-phase sampling design	Sampling design methodology	Probability sampling design
Основоположне джерело	Neyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association, 33(201), 101–116.	McIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Інші назви	Two-Phase Sampling	RSS	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Пов'язані	4	4	5
Підсумок	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.	Ranked Set Sampling (RSS) is a data collection method introduced by G. A. McIntyre in 1952 that improves estimation efficiency when visual ranking of units is easier or cheaper than actual measurement. By deliberately selecting and measuring units that are ranked as most likely to yield desired outcomes, RSS reduces variance compared to simple random sampling while maintaining unbiasedness.	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.