Bandingkan kaedah

	Persampelan Berpemberat Adaptif	Pensampelan Berperingkat
Bidang	Metodologi Tinjauan	Metodologi Tinjauan
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1990s–2000s	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
Pengasas	Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature	Leslie Kish; William G. Cochran
Jenis	Probabilistic sampling procedure	Probability sampling design
Sumber perintis	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495
Alias	AWS, adaptive importance sampling, sequential adaptive weighting, dynamic weighted sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
Berkaitan	6	5
Ringkasan	Adaptive weighted sampling is a probabilistic sampling procedure that assigns and iteratively updates inclusion weights for population units based on observed data collected during the sampling process itself. Unlike static weighted sampling — where weights are fixed before data collection from known auxiliary information — adaptive weighting revises probabilities as new information accumulates, concentrating sampling effort on units that contribute most to estimating the target quantity. It is used in survey methodology, simulation studies, and rare-event estimation.	Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage.