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第1行： − + − Graphic breakdown of stratified random sampling − − 分层随机抽样的图解 第10行：
第7行： − 在统计学中，分层随机抽样是一种抽样方法，首先将整个研究人口分层为具有相同属性或特征的子群，称为分层，然后从分层组中进行简单随机抽样，在抽样过程的任何阶段，随机、完全随机地无偏选择同一子群中的每一个元素。分层随机化被认为是分层抽样的一个细分，当共享属性部分存在并且在被调查人口的不同亚群之间有很大差异时，应该采用，因此在抽样时需要特殊的考虑或者明确的区分。这种抽样方法应区别于整群抽样方法，整群抽样方法是在整个群体中选择一个简单的随机抽样来代表整个总体，或分层系统抽样方法，在分层过程之后进行系统抽样。分层随机抽样有时也称为“定额随机抽样”。+
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[[File:Graphic_breakdown_of_stratified_random_sampling.jpeg|thumb|220x220px|Graphic breakdown of stratified random sampling]]
[[File:Graphic_breakdown_of_stratified_random_sampling.jpeg|thumb|220x220px|分层随机抽样的图形分解 Graphic breakdown of stratified random sampling]]
In [[statistics]], '''stratified randomization''' is a method of [[Sampling (statistics)|sampling]] which first stratifies the whole study [[Statistical population|population]] into [[Statistical population|subgroups]] with same [[Variable and attribute (research)|attributes]] or characteristics, known as strata, then followed by [[Simple random sample|simple random sampling]] from the stratified groups, where each element within the same subgroup are selected [[Bias (statistics)|unbiasedly]] during any stage of the sampling process, randomly and entirely by chance.<ref name=":3" /><ref>{{Citation|title=Simple random sample|date=2020-03-18|url=https://en.wikipedia.org/w/index.php?title=Simple_random_sample&oldid=946144051|work=Wikipedia|language=en|access-date=2020-04-07}}</ref> Stratified randomization is considered a subdivision of [[stratified sampling]], and should be adopted when shared attributes exist partially and vary widely between subgroups of the investigated population, so that they require special considerations or clear distinctions during sampling.<ref>{{Citation|title=Stratified sampling|date=2020-02-09|url=https://en.wikipedia.org/w/index.php?title=Stratified_sampling&oldid=939938944|work=Wikipedia|language=en|access-date=2020-04-07}}</ref> This sampling method should be distinguished from [[cluster sampling]], where a simple random sample of several entire clusters is selected to represent the whole population, or stratified systematic sampling, where a [[systematic sampling]] is carried out after the stratification process. Stratified random sampling is sometimes also known as "'''quota random sampling'''".<ref name=":3">{{Cite web|url=https://www.investopedia.com/ask/answers/032615/what-are-some-examples-stratified-random-sampling.asp|title=How Stratified Random Sampling Works|last=Nickolas|first=Steven|date=July 14, 2019|website=Investopedia|language=en|access-date=2020-04-07}}</ref>
In [[statistics]], '''stratified randomization''' is a method of [[Sampling (statistics)|sampling]] which first stratifies the whole study [[Statistical population|population]] into [[Statistical population|subgroups]] with same [[Variable and attribute (research)|attributes]] or characteristics, known as strata, then followed by [[Simple random sample|simple random sampling]] from the stratified groups, where each element within the same subgroup are selected [[Bias (statistics)|unbiasedly]] during any stage of the sampling process, randomly and entirely by chance.<ref name=":3" /><ref>{{Citation|title=Simple random sample|date=2020-03-18|url=https://en.wikipedia.org/w/index.php?title=Simple_random_sample&oldid=946144051|work=Wikipedia|language=en|access-date=2020-04-07}}</ref> Stratified randomization is considered a subdivision of [[stratified sampling]], and should be adopted when shared attributes exist partially and vary widely between subgroups of the investigated population, so that they require special considerations or clear distinctions during sampling.<ref>{{Citation|title=Stratified sampling|date=2020-02-09|url=https://en.wikipedia.org/w/index.php?title=Stratified_sampling&oldid=939938944|work=Wikipedia|language=en|access-date=2020-04-07}}</ref> This sampling method should be distinguished from [[cluster sampling]], where a simple random sample of several entire clusters is selected to represent the whole population, or stratified systematic sampling, where a [[systematic sampling]] is carried out after the stratification process. Stratified random sampling is sometimes also known as "'''quota random sampling'''".<ref name=":3">{{Cite web|url=https://www.investopedia.com/ask/answers/032615/what-are-some-examples-stratified-random-sampling.asp|title=How Stratified Random Sampling Works|last=Nickolas|first=Steven|date=July 14, 2019|website=Investopedia|language=en|access-date=2020-04-07}}</ref>
In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup are selected unbiasedly during any stage of the sampling process, randomly and entirely by chance. Stratified randomization is considered a subdivision of stratified sampling, and should be adopted when shared attributes exist partially and vary widely between subgroups of the investigated population, so that they require special considerations or clear distinctions during sampling. This sampling method should be distinguished from cluster sampling, where a simple random sample of several entire clusters is selected to represent the whole population, or stratified systematic sampling, where a systematic sampling is carried out after the stratification process. Stratified random sampling is sometimes also known as "quota random sampling".
In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup are selected unbiasedly during any stage of the sampling process, randomly and entirely by chance. Stratified randomization is considered a subdivision of stratified sampling, and should be adopted when shared attributes exist partially and vary widely between subgroups of the investigated population, so that they require special considerations or clear distinctions during sampling. This sampling method should be distinguished from cluster sampling, where a simple random sample of several entire clusters is selected to represent the whole population, or stratified systematic sampling, where a systematic sampling is carried out after the stratification process. Stratified random sampling is sometimes also known as "quota random sampling".
在统计学中，<font color="#ff8000"> '''分层随机试验 Stratified randomization''' </font>是一种抽样方法，首先将整个研究<font color="#ff8000"> '''总体 Population''' </font>层为具有相同属性或特征的子群，称为<font color="#ff8000"> '''分层 Attributes''' </font>，然后从分层组中进行简单随机抽样，在抽样过程的任何阶段，随机、完全偶然地无偏抽取同一子群中的元素。<ref name=":3" /><ref>{{Citation|title=Simple random sample|date=2020-03-18|url=https://en.wikipedia.org/w/index.php?title=Simple_random_sample&oldid=946144051|work=Wikipedia|language=en|access-date=2020-04-07}}</ref>分层随机试验被认为是<font color="#ff8000"> '''分层抽样 Stratified sampling''' </font>的一个细分。当共享属性部分存在，并且在被调查总体的不同亚群之间有很大差异时，应该采用分层随机试验。因此，在取样过程中需要特别考虑或明确区分。<ref>{{Citation|title=Stratified sampling|date=2020-02-09|url=https://en.wikipedia.org/w/index.php?title=Stratified_sampling&oldid=939938944|work=Wikipedia|language=en|access-date=2020-04-07}}</ref>这种抽样方法应区别于<font color="#ff8000"> '''整群抽样方法 Cluster sampling''' </font>，整群抽样方法是在整个群体中选择一个简单的随机抽样来代表整个总体，或分层系统抽样方法，在分层过程之后进行<font color="#ff8000"> '''系统抽样 Systematic sampling''' </font>。分层随机抽样有时也称为<font color="#ff8000"> '''定额随机抽样 Quota random sampling''' </font>。