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简单随机试验被认为是在每个阶层中分配受试者的最简单方法。对于每个任务，受试者被完全随机地分配到每个组中。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。尽管很容易进行，但简单随机试验通常应用于包含 100 个以上样本的层，因为小样本量会使分配不均等。
简单随机试验被认为是在每个阶层中分配受试者的最简单方法。对于每个任务，受试者被完全随机地分配到每个组中。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。尽管很容易进行，但简单随机试验通常应用于包含 100 个以上样本的层，因为小样本量会使分配不均等。
===Block randomization within strata===
[[Randomized block design|Block randomization]], sometimes called permuted block randomization, applies blocks to allocate subjects from the same strata equally to each group in the study. In block randomization, allocation ratio (ratio of the number of one specific group over other groups) and group sizes are specified. The block size must be the multiples of the number of treatments so that samples in each stratum can be assigned to treatment groups with the intended ratio.<ref name=":0" /> For instance, there should be 4 or 8 strata in a clinical trial concerning breast cancer where age and nodal statuses are two prognostic factors and each factor is split into two-level. The different blocks can be assigned to samples in multiple ways including random list and computer programming.<ref>{{Cite web|url=https://www.sealedenvelope.com/help/redpill/latest/block/|title=Sealed Envelope {{!}} Random permuted blocks|date=Feb 25, 2020|website=www.sealedenvelope.com|access-date=2020-04-07}}</ref><ref>{{Citation|last1=Friedman|first1=Lawrence M.|title=Introduction to Clinical Trials|date=2010|work=Fundamentals of Clinical Trials|pages=1–18|publisher=Springer New York|isbn=978-1-4419-1585-6|last2=Furberg|first2=Curt D.|last3=DeMets|first3=David L.|doi=10.1007/978-1-4419-1586-3_1}}</ref>
Block randomization is commonly used in the experiment with a relatively big sampling size to avoid the imbalance allocation of samples with important characteristics. In certain fields with strict requests of randomization such as clinical trials, the allocation would be predictable when there is no blinding process for conductors and the block size is limited. The blocks permuted randomization in strata could possibly cause an imbalance of samples among strata as the number of strata increases and the sample size is limited, For instance, there is a possibility that no sample is found meeting the characteristic of certain strata.
为了避免重要特征样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。
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.
在临床试验中，混杂因素是需要考虑的重要因素
在临床试验中，混杂因素是需要考虑的重要因素
There is also a higher level of statistical accuracy for stratified random sampling compared with simple random sampling, due to the high relevance of elements chosen to represent the population. The step of stratified randomization is extremely important as an attempt to ensure that no bias, delibrate or accidental, affects the representative nature of the patient sample under study. It increases the study power, especially in small clinical trials(n<400), as these known clinical traits stratified are thought to effect the outcomes of the interventions. It helps prevent the occurrence of type I error, which is valued highly in clinical studies. It also has an important effect on sample size for active control equivalence trials and in theory, facilitates subgroup analysis and interim analysis.
There is also a higher level of statistical accuracy for stratified random sampling compared with simple random sampling, due to the high relevance of elements chosen to represent the population. The step of stratified randomization is extremely important as an attempt to ensure that no bias, delibrate or accidental, affects the representative nature of the patient sample under study. It increases the study power, especially in small clinical trials(n<400), as these known clinical traits stratified are thought to effect the outcomes of the interventions. It helps prevent the occurrence of type I error, which is valued highly in clinical studies. It also has an important effect on sample size for active control equivalence trials and in theory, facilitates subgroup analysis and interim analysis.