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第111行： − − ==应用 Application== − − [[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]] − − 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> − 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> − − − 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> − − 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'''<font color="#ff8000"> 混杂因素 Confounder </font>'''时，它也很有用，因为分层随机试验允许调整'''<font color="#ff8000"> 协方差 Covariances </font>'''和 '''<font color="#ff8000"> p 值 p-values </font>'''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> − − 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. − − 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，'''<font color="#ff8000"> 标准差 Standard deviation </font>'''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'''<font color="#ff8000"> 成本效益 Cost-effectiveness </font>'''。 − − 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" />
→应用 Application
简单随机化被认为是最简单的方法分配主体在每个阶层。每次分配的主题都是随机分配给每个小组的。尽管简单的随机化方法易于实施，但是由于小样本容易造成分配不等，因此在样本数超过100个的地层中常常采用简单的随机化方法。
简单随机化被认为是最简单的方法分配主体在每个阶层。每次分配的主题都是随机分配给每个小组的。尽管简单的随机化方法易于实施，但是由于小样本容易造成分配不等，因此在样本数超过100个的地层中常常采用简单的随机化方法。
==临床试验中的分层随机试验 Stratified randomization in clinical trials==
==临床试验中的分层随机试验 Stratified randomization in clinical trials==