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第94行： − # The subgroup size is taken to be of the same importance if the data available cannot represent overall subgroup population. In some applications, subgroup size is decided with reference to the amount of data available instead of scaling sample sizes to subgroup size, which would introduce bias in the effects of factors. In some cases that data needs to be stratified by variances, subgroup variances differ significantly, making each subgroup sampling size proportional to the overall subgroup population cannot be guaranteed.<ref name=":2"> − {{Citation|last1=Glass|first1=Aenne|title=Potential Advantages and Disadvantages of Stratification in Methods of Randomization|date=2014|work=Springer Proceedings in Mathematics & Statistics|pages=239–246|publisher=Springer New York|isbn=978-1-4939-2103-4|last2=Kundt|first2=Guenther|doi=10.1007/978-1-4939-2104-1_23}}</ref> − # Stratified sampling can not be applied if the population cannot be completely assigned into strata, which would result in sample sizes proportional to sample available instead of overall subgroup population.<ref name=":0" /> − # The process of assigning samples into subgroups could involve overlapping if subjects meet the inclusion standard of multiple strata, which could result in a misrepresentation of the population.<ref name=":2" />+
→缺点 Disadvantage
{{Citation|last1=Glass|first1=Aenne|title=Potential Advantages and Disadvantages of Stratification in Methods of Randomization|date=2014|work=Springer Proceedings in Mathematics & Statistics|pages=239–246|publisher=Springer New York|isbn=978-1-4939-2103-4|last2=Kundt|first2=Guenther|doi=10.1007/978-1-4939-2104-1_23}}</ref>
{{Citation|last1=Glass|first1=Aenne|title=Potential Advantages and Disadvantages of Stratification in Methods of Randomization|date=2014|work=Springer Proceedings in Mathematics & Statistics|pages=239–246|publisher=Springer New York|isbn=978-1-4939-2103-4|last2=Kundt|first2=Guenther|doi=10.1007/978-1-4939-2104-1_23}}</ref>
#如果人口不能完全分配到层中，则不能应用分层抽样，这将导致样本大小与可用样本成正比，而不是与总体子组人口成正比。<ref name=":0" />
#如果人口不能完全分配到层中，则不能应用分层抽样，这将导致样本大小与可用样本成正比，而不是与总体子组人口成正比。<ref name=":0" />
#如果受试者符合多层次的纳入标准，则将样本分配到亚组的过程可能涉及重叠，这可能导致总体的错误陈述。<ref name=":2">{{Citation|last1=Glass|first1=Aenne|title=Potential Advantages and Disadvantages of Stratification in Methods of Randomization|date=2014|work=Springer Proceedings in Mathematics & Statistics|pages=239–246|publisher=Springer New York|isbn=978-1-4939-2103-4|last2=Kundt|first2=Guenther|doi=10.1007/978-1-4939-2104-1_23}}</ref>
#如果受试者符合多层次的纳入标准，则将样本分配到亚组的过程可能涉及重叠，这可能导致总体的错误陈述。<ref name=":2">{{Citation|last1=Glass|first1=Aenne|title=Potential Advantages and Disadvantages of Stratification in Methods of Randomization|date=2014|work=Springer Proceedings in Mathematics & Statistics|pages=239–246|publisher=Springer New York|isbn=978-1-4939-2103-4|last2=Kundt|first2=Guenther|doi=10.1007/978-1-4939-2104-1_23}}</ref>