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#如果可用数据不能代表整个亚组总体，则认为亚组大小具有相同的重要性。在某些应用中，子组大小是根据可用数据量来决定的，而不是将样本大小缩放到子组大小，这会在因子效应中引入偏差。在某些需要对数据进行方差分层的情况下，子组方差差异显着，使得每个子组的抽样规模无法保证与整个子组总体成正比。 [19]

#如果可用数据不能代表整个亚组总体，则认为亚组大小具有相同的重要性。在某些应用中，子组大小是根据可用数据量来决定的，而不是将样本大小缩放到子组大小，这会在因子效应中引入偏差。在某些需要对数据进行方差分层的情况下，子组方差差异显着，使得每个子组的抽样规模无法保证与整个子组总体成正比。 [19]

{{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>

# 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>

 

# 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" />

# 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" />

#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。 [7]

#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。 [7]

# 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" />

# 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" />

# It is easier for a team to be trained to stratify a sample because of the exactness of the nature of stratified randomization.<ref name=":0" />

# It is easier for a team to be trained to stratify a sample because of the exactness of the nature of stratified randomization.<ref name=":0" />