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第155行： − − ==缺点 Disadvantage == − − The limits of stratified randomization include: − 分层随机试验的限制包括： − # Stratified randomization firstly divides samples into several strata with reference to prognostic factors but there is possible that the samples are unable to be divided. In application, the significance of prognostic factors lacks strict approval in some cases, which could further result in bias. This is why the factors' potential for making effects to result should be checked before the factors are included in stratification. In some cases that the impact of factors on the outcome cannot be approved, unstratified randomization is suggested.<ref>{{Cite web|url=https://www.investopedia.com/ask/answers/041615/what-are-advantages-and-disadvantages-stratified-random-sampling.asp|title=Pros and Cons of Stratified Random Sampling|last=Murphy|first=Chris B.|date=Apr 13, 2019|website=Investopedia|language=en|access-date=2020-04-07}}</ref> − − #分层随机试验首先参考预后因素将样本分成若干层，但有可能无法划分样本。在应用中，在某些情况下，预后因素的重要性缺乏严格的认可，这可能进一步导致偏差。这就是为什么在将因素纳入分层之前应该检查因素产生影响的潜力的原因。在某些因素对结果的影响无法得到批准（approved）的情况下，建议进行无分层随机试验。 <ref>{{Cite web|url=https://www.investopedia.com/ask/answers/041615/what-are-advantages-and-disadvantages-stratified-random-sampling.asp|title=Pros and Cons of Stratified Random Sampling|last=Murphy|first=Chris B.|date=Apr 13, 2019|website=Investopedia|language=en|access-date=2020-04-07}}</ref> − − #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> − − # 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" /> − #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" /> − #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"> − − − # 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" /> − # Researchers can get highly useful results by analyzing smaller sample sizes because of statistical accuracy of this method. − # This sampling technique covers a wide range of population since complete charge over the strata division has been made. − # Sometimes stratified randomization is desirable to have estimates of population parameters for groups within the population.<ref name=":1" />
→缺点 Disadvantage
# Stratified randomization can accurately reflect the outcomes of the general population since influential factors are applied to stratify the entire samples and balance the samples' vital characteristics among treatment groups. For instance, applying stratified randomization to make a sample of 100 from the population can guarantee the balance of males and females in each treatment group, while using simple randomization might result in only 20 males in one group and 80 males in another group.<ref name=":0" />
# Stratified randomization can accurately reflect the outcomes of the general population since influential factors are applied to stratify the entire samples and balance the samples' vital characteristics among treatment groups. For instance, applying stratified randomization to make a sample of 100 from the population can guarantee the balance of males and females in each treatment group, while using simple randomization might result in only 20 males in one group and 80 males in another group.<ref name=":0" />
# Stratified randomization makes a smaller error than other sampling methods such as [[cluster sampling]], simple random sampling, and [[systematic sampling]] or [http://dissertation.laerd.com/non-probability-sampling.php non-probability methods] since measurements within strata could be made to have a lower [[standard deviation]]. Randomizing divided strata are more manageable and cheaper in some cases than simply randomizing general samples.<ref name=":1" />
# Stratified randomization makes a smaller error than other sampling methods such as [[cluster sampling]], simple random sampling, and [[systematic sampling]] or [http://dissertation.laerd.com/non-probability-sampling.php non-probability methods] since measurements within strata could be made to have a lower [[standard deviation]]. Randomizing divided strata are more manageable and cheaper in some cases than simply randomizing general samples.<ref name=":1" />