পদ্ধতির তুলনা করুন

	Ranked Set Sampling	স্তরবিন্যস্ত নমুনায়ন (Stratified Sampling)	Systematic Sampling
ক্ষেত্র	নমুনায়ন	জরিপ পদ্ধতি	জরিপ পদ্ধতি
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1952	1977	Mid-20th century (Cochran 1953; Kish 1965)
প্রবর্তক	Glenn A. McIntyre	William G. Cochran	William G. Cochran; formalized in survey sampling theory
ধরন	Sampling design methodology	Probability-based survey sampling design	Probability sampling design
মৌলিক উৎস	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.). Wiley. ISBN: 978-0-471-16240-7	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
অপর নাম	RSS	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
সম্পর্কিত	4	2	5
সারসংক্ষেপ	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.	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.