Vertaile menetelmiä

	Merkintä-uudelleennäytteenotto (Capture-Recapture) -populaatioestimaatti	Stratifioitu otanta	Kyselypainotus ja kalibrointi
Tieteenala	Kyselytutkimuksen metodologia	Kyselytutkimuksen metodologia	Kyselytutkimuksen metodologia
Menetelmäperhe	Regression model	Process / pipeline	Process / pipeline
Syntyvuosi	1978	1977	2010
Kehittäjä	Otis, Burnham, White & Anderson	William G. Cochran	Sharon Lohr
Tyyppi	Probabilistic population size estimator	Probability-based survey sampling design	Estimation adjustment procedure
Alkuperäislähde	Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, 62, 3–135.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7	Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5
Rinnakkaisnimet	Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)
Liittyvät	2	2	3
Tiivistelmä	Capture-recapture (also known as mark-recapture) is a statistical method for estimating the size of an unknown population by sampling it twice and tracking which individuals appear in both samples. Formally systematized for closed animal populations by Otis, Burnham, White, and Anderson in their landmark 1978 Wildlife Monographs paper, the method extends naturally to human populations, epidemiology, and incomplete administrative records.	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.	Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.