विधियों की तुलना करें

	अनुकूली सरल यादृच्छिक नमूनाकरण	अनुकूली क्लस्टर नमूनाकरण
क्षेत्र	सर्वेक्षण पद्धति	सर्वेक्षण पद्धति
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1990–1992	1990
प्रवर्तक	Steven K. Thompson	Steven K. Thompson
प्रकार	Probability-based adaptive sampling design	Probability-based adaptive sampling design
मौलिक स्रोत	Thompson, S. K. (1992). Sampling. John Wiley & Sons. ISBN: 978-0471548850	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
उपनाम	ASRS, adaptive SRS, adaptive random sampling, sequential adaptive sampling	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling
संबंधित	5	6
सारांश	Adaptive simple random sampling (ASRS) begins with a conventional simple random sample and then expands the sample in regions where the variable of interest exceeds a pre-specified threshold. Units neighboring a qualifying observation are added to the sample, allowing the design to concentrate effort where the population is dense or rare, while retaining unbiased estimation through the Horvitz-Thompson or Hansen-Hurwitz estimators. The approach was systematized by Steven K. Thompson in the early 1990s as part of the broader adaptive sampling framework.	Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups.