Linganisha mbinu

	Sampuli yenye Safu Iliyoainishwa	Sampuli Pacha	Sampuli Iliyowekwa Ngazi
Nyanja	Usampulishaji	Usampulishaji	Metodolojia ya Dodoso
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1952	1938	1977
Mwanzilishi	Glenn A. McIntyre	Jerzy Neyman	William G. Cochran
Aina	Sampling design methodology	Multi-phase sampling design	Probability-based survey sampling design
Chanzo asilia	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.). Wiley. ISBN: 978-0-471-16240-7
Majina mbadala	RSS	Two-Phase Sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Zinazohusiana	4	4	2
Muhtasari	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.	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.