{"title":"Testing for normality: a user's (cautionary) guide.","authors":"Romain-Daniel Gosselin","doi":"10.1177/00236772241276808","DOIUrl":null,"url":null,"abstract":"<p><p>The normality assumption postulates that empirical data derives from a normal (Gaussian) population. It is a pillar of inferential statistics that enables the theorization of probability functions and the computation of p-values thereof. The breach of this assumption may not impose a formal mathematical constraint on the computation of inferential outputs (e.g., p-values) but may make them inoperable and possibly lead to unethical waste of laboratory animals. Various methods, including statistical tests and qualitative visual examination, can reveal incompatibility with normality and the choice of a procedure should not be trivialized. The following minireview will provide a brief overview of diagrammatical methods and statistical tests commonly employed to evaluate congruence with normality. Special attention will be given to the potential pitfalls associated with their application. Normality is an unachievable ideal that practically never accurately describes natural variables, and detrimental consequences of non-normality may be safeguarded by using large samples. Therefore, the very concept of preliminary normality testing is also, arguably provocatively, questioned.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laboratory Animals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/00236772241276808","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The normality assumption postulates that empirical data derives from a normal (Gaussian) population. It is a pillar of inferential statistics that enables the theorization of probability functions and the computation of p-values thereof. The breach of this assumption may not impose a formal mathematical constraint on the computation of inferential outputs (e.g., p-values) but may make them inoperable and possibly lead to unethical waste of laboratory animals. Various methods, including statistical tests and qualitative visual examination, can reveal incompatibility with normality and the choice of a procedure should not be trivialized. The following minireview will provide a brief overview of diagrammatical methods and statistical tests commonly employed to evaluate congruence with normality. Special attention will be given to the potential pitfalls associated with their application. Normality is an unachievable ideal that practically never accurately describes natural variables, and detrimental consequences of non-normality may be safeguarded by using large samples. Therefore, the very concept of preliminary normality testing is also, arguably provocatively, questioned.
正态性假设假定经验数据来自正常(高斯)群体。它是推理统计的支柱,使概率函数的理论化及其 p 值的计算成为可能。违反这一假设可能不会对推理输出(如 p 值)的计算造成正式的数学限制,但可能会使其无法操作,并可能导致实验动物的不道德浪费。各种方法,包括统计检验和定性直观检查,都可以发现不符合正态性的情况,因此不应轻视程序的选择。下面的小视图将简要介绍常用于评估是否符合正态性的图解法和统计检验法。我们将特别关注与这些方法的应用相关的潜在隐患。正态性是一个无法实现的理想,实际上永远无法准确描述自然变量,而使用大样本可以避免非正态性的有害后果。因此,初步正态性检验的概念本身也受到了质疑,可以说是挑衅性的质疑。
期刊介绍:
The international journal of laboratory animal science and welfare, Laboratory Animals publishes peer-reviewed original papers and reviews on all aspects of the use of animals in biomedical research. The journal promotes improvements in the welfare or well-being of the animals used, it particularly focuses on research that reduces the number of animals used or which replaces animal models with in vitro alternatives.