B. Manly
{"title":"Randomization","authors":"B. Manly","doi":"10.1002/wics.91","DOIUrl":null,"url":null,"abstract":"There are three aspects of randomization in statistics that are considered here. The first aspect is randomization as part of a sampling design to estimate one or more parameters for a statistical population such as all the farms in a certain area of a country, based on information obtained about the parameters from only a part of the population. The second aspect is using randomization as part of an experimental design to ensure that the allocation of treatment levels to the experimental units is not biased in any way. For example, the test of a new drug for relieving the symptoms of a disease might involve this drug being randomly allocated to half of a group of patients, while the other half of the patients receive a standard drug that is used for the disease. Finally, the third aspect is using randomization to test some statistical hypothesis. For example, to see if there is a significant difference between two drugs for the treatment of a disease in terms of some suitable outcome measure, the observed mean difference between means for this outcome measure might be compared to the distribution of mean differences that is obtained by randomly reallocating the observed values of the measure to the drugs. The null hypothesis being tested would then be that each of the observed values of the measure was equally likely to have occurred with each of the two drugs. Copyright © 2010 John Wiley & Sons, Inc.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"30 1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"79","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews-Computational Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/wics.91","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 79
随机化
这里考虑了统计学中随机化的三个方面。第一个方面是随机化,作为抽样设计的一部分,根据仅从一部分人口中获得的有关参数的信息,估计统计总体(如一个国家某一地区的所有农场)的一个或多个参数。第二个方面是使用随机化作为实验设计的一部分,以确保分配给实验单位的治疗水平没有任何偏差。例如,一种用于缓解疾病症状的新药的测试可能涉及将这种药物随机分配给一组患者中的一半,而另一半患者则使用用于该疾病的标准药物。最后,第三个方面是使用随机化来检验一些统计假设。例如,为了确定治疗某种疾病的两种药物在某些合适的结果度量方面是否存在显著差异,可以将该结果度量的观察到的均值之间的平均差异与通过随机将该度量的观察值重新分配给药物而获得的均值差异的分布进行比较。被检验的原假设是,测量的每个观察值在两种药物中出现的可能性是一样的。版权所有©2010约翰威利父子公司。
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