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第39行： + + + − − Simple randomization is considered as the easiest method for allocating subjects in each stratum. Subjects are assigned to each group purely randomly for every assignment. Even though it is easy to conduct, simple randomization is commonly applied in strata that contain more than 100 samples since a small sampling size would make assignment unequal.<ref name=":0" />
→Simple randomization within strata
在每一层中，可以应用几种随机试验策略，包括<font color="#ff8000"> '''简单随机试验 Simple randomization''' </font>、<font color="#ff8000"> '''分块随机试验你Blocked randomization''' </font>和<font color="#ff8000"> '''最小化试验 Minimization''' </font>。
在每一层中，可以应用几种随机试验策略，包括<font color="#ff8000"> '''简单随机试验 Simple randomization''' </font>、<font color="#ff8000"> '''分块随机试验你Blocked randomization''' </font>和<font color="#ff8000"> '''最小化试验 Minimization''' </font>。
=== Simple randomization within strata ===
=== Simple randomization within strata ===
Simple randomization is considered as the easiest method for allocating subjects in each stratum. Subjects are assigned to each group purely randomly for every assignment. Even though it is easy to conduct, simple randomization is commonly applied in strata that contain more than 100 samples since a small sampling size would make assignment unequal.<ref name=":0" />
简单的随机化被认为是最简单的方法来分配受试者在每个阶层。受试者被随机分配到每组。尽管简单的随机化很容易进行，但由于取样量小，分配不均，因此在含有100多个样本的地层中，通常采用简单的随机化方法。
Stratified random sampling is useful and productive in situations requiring different weightings 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.
Stratified random sampling is useful and productive in situations requiring different weightings 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.
分层随机抽样在特定地层需要不同权重的情况下是有用的和有效的。通过这种方式，研究人员可以操纵来自每个阶层的选择机制，以便在调查结果中放大或减少所需的特征。
分层随机抽样在特定地层需要不同权重的情况下是有用的和有效的。通过这种方式，研究人员可以操纵来自每个阶层的选择机制，以便在调查结果中放大或减少所需的特征。