Jämför metoder

	Dubbel sampling	Ranked Set Sampling	Systematisk stickprovsuttagning
Ämnesområde	Urvalsmetodik	Urvalsmetodik	Surveymetodik
Familj	Process / pipeline	Process / pipeline	Process / pipeline
Ursprungsår	1938	1952	Mid-20th century (Cochran 1953; Kish 1965)
Upphovsperson	Jerzy Neyman	Glenn A. McIntyre	William G. Cochran; formalized in survey sampling theory
Typ	Multi-phase sampling design	Sampling design methodology	Probability sampling design
Ursprungskälla	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
Alias	Two-Phase Sampling	RSS	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Närliggande	4	4	5
Sammanfattning	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.