So sánh phương pháp

	Lấy mẫu có trọng số thí điểm	Lấy mẫu có trọng số thích ứng
Lĩnh vực	Phương pháp luận khảo sát	Phương pháp luận khảo sát
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Mid-20th century (classical weighted sampling ~1934–1977; pilot study integration formalized in survey practice ~1970s–1980s)	1990s–2000s
Người khởi xướng	Cochran, W. G.; Neyman, J.	Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature
Loại	Probability sampling with differential selection probabilities in a preliminary study phase	Probabilistic sampling procedure
Công trình gốc	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
Tên gọi khác	pilot phase weighted sampling, weighted pilot sampling, pilot probability proportional sampling, pilot PPS sampling	AWS, adaptive importance sampling, sequential adaptive weighting, dynamic weighted sampling
Liên quan	3	6
Tóm tắt	Pilot weighted sampling applies weighted (unequal-probability) sampling within a small-scale preliminary study to estimate key design parameters — variance components, design effects, and optimal stratum weights — before committing resources to the full survey. By using differential inclusion probabilities in the pilot, researchers obtain more precise parameter estimates for rarer or more variable subgroups while keeping total pilot cost low. The results directly inform the weighting scheme and sample-size allocation for the main survey.	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.