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分层随机试验 (查看源代码)

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[[File:Confounding_factors_are_important_to_consider_in_clinical_trials.png|thumb|219x219px|混杂因素在临床试验中很重要]]

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Stratified random sampling is useful and productive in situations requiring different [[weighting]]s on specific strata. In this way, the researchers can manipulate the selection mechanisms from each strata to amplify or minimize the desired characteristics in the survey result.<ref>{{Cite web|url=https://www.thoughtco.com/stratified-sampling-3026731|title=Understanding Stratified Samples and How to Make Them|last=Crossman|first=Ashley|date=Jan 27, 2020|website=ThoughtCo|language=en|access-date=2020-04-07}}</ref>

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分层随机试验在需要对特定层进行不同权重的情况下非常有用且富有成效。通过这种方式，研究人员可以操纵每个层次的选择机制，以放大或最小化调查结果中所需的特征。<ref>{{Cite web|url=https://www.thoughtco.com/stratified-sampling-3026731|title=Understanding Stratified Samples and How to Make Them|last=Crossman|first=Ashley|date=Jan 27, 2020|website=ThoughtCo|language=en|access-date=2020-04-07}}</ref>

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Stratified randomization is helpful when researchers intend to seek for [[Association (statistics)|associations]] between two or more strata, as simple random sampling causes a larger chance of unequal representation of target groups. It is also useful when the researchers wish to eliminate [[Confounding|confounders]] in [[Observational study|observational studies]] as stratified random sampling allows the adjustments of [[covariance]]s and the [[P-value|''p''-values]] for more accurate results.<ref>{{Cite book|last=Hennekens, Charles H.|title=Epidemiology in medicine|date=1987|publisher=Little, Brown|others=Buring, Julie E., Mayrent, Sherry L.|isbn=0-316-35636-0|edition=1st|location=Boston, Massachusetts|oclc=16890223}}</ref>

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当研究人员打算寻找两个或多个层次之间的关联时，分层随机化很有帮助，因为简单的随机抽样会导致更大的可能出现目标群体的不平等代表性。当研究人员希望消除观察性研究中的''' 混杂因素 Confounder '''时，它也很有用，因为分层随机试验允许调整''' 协方差 Covariances '''和 ''' p 值 p-values '''以获得更准确的结果。 <ref>{{Cite book|last=Hennekens, Charles H.|title=Epidemiology in medicine|date=1987|publisher=Little, Brown|others=Buring, Julie E., Mayrent, Sherry L.|isbn=0-316-35636-0|edition=1st|location=Boston, Massachusetts|oclc=16890223}}</ref>

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There is also a higher level of [[Accuracy and precision|statistical accuracy]] for stratified random sampling compared with simple random sampling, due to the high [[relevance]] of elements chosen to represent the population.<ref name=":5" /> The differences within the strata is much less compared to the one between strata. Hence, as the between-sample differences are minimized, the [[standard deviation]] will be consequently tightened, resulting in higher degree of accuracy and small error in the final results. This effectively reduces the [[Sample size determination|sample size]] needed and increases [[Cost-effectiveness analysis|cost-effectiveness]] of sampling when research funding is tight.

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与简单随机抽样相比，分层随机抽样的统计准确度也更高，因为选择代表总体的元素具有高度相关性。<ref name=":5" />与分层之间的差异相比，分层内的差异要小得多。因此，随着样本间差异的最小化，''' 标准差 Standard deviation '''也会随之收紧，从而导致最终结果的准确性更高，误差更小。当研究资金紧张时，这有效地减少了所需的样本量并提高了抽样的''' 成本效益 Cost-effectiveness '''。

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In real life, stratified random sampling can be applied to results of election polling, investigations into income disparities among social groups, or measurements of education opportunities across nations.<ref name=":3" />

在现实生活中，分层随机试验可应用于选举投票结果、社会群体收入差距调查或各国教育机会的衡量。 <ref name=":3" />

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本分配不平衡的问题，实验中常采用分块随机化的方法，采样规模较大。在某些严格要求随机化的领域，例如临床试验，当没有导体的盲法和块大小有限时，分配是可以预测的。随着地层数量的增加和样本容量的限制，地层中的块体随机化可能导致地层之间样本的不平衡，例如，有可能找不到符合特定地层特征的样本。

孙钦贵

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