Porównaj metody

	Próba uporządkowana	Podwójne pobieranie prób	Próba systematyczna
Dziedzina	Dobór próby	Dobór próby	Metodologia badań sondażowych
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	1952	1938	Mid-20th century (Cochran 1953; Kish 1965)
Twórca	Glenn A. McIntyre	Jerzy Neyman	William G. Cochran; formalized in survey sampling theory
Typ	Sampling design methodology	Multi-phase sampling design	Probability sampling design
Źródło pierwotne	McIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390.	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.). John Wiley & Sons. ISBN: 978-0471162407
Inne nazwy	RSS	Two-Phase Sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Pokrewne	4	4	5
Podsumowanie	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.	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.	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.