Laboratory Animals最新文献_第6页

About statistical significance, and the lack thereof. 关于统计意义，以及缺乏统计意义的问题。

IF 1.3 4区农林科学

Laboratory Animals Pub Date : 2024-10-01 Epub Date: 2024-08-19 DOI: 10.1177/00236772241248509

Fulvio Magara, Benjamin Boury-Jamot

{"title":"About statistical significance, and the lack thereof.","authors":"Fulvio Magara, Benjamin Boury-Jamot","doi":"10.1177/00236772241248509","DOIUrl":"10.1177/00236772241248509","url":null,"abstract":"Absence of statistical significance (i.e., p > 0.05) in the results of a frequentist test comparing two samples is often used as evidence of absence of difference, or absence of effect of a treatment, on the measured variable. Such conclusions are often wrong because absence of significance may merely result from a sample size that is too small to reveal an effect. To conclude that there is no meaningful effect of a treatment/condition, it is necessary to use an appropriate statistical approach. For frequentist statistics, a simple tool for this goal is the 'two one-sided t-test,' a form of equivalence test that relies on the a priori definition of a minimal difference considered to be relevant. In other words, the smallest effect size of interest should be established in advance. We present the principles of this test and give examples where it allows correct interpretation of the results of a classical t-test assuming absence of difference. Equivalence tests are also very useful in probing whether certain significant results are also biologically meaningful, because when comparing large samples it is possible to find significant results in both an equivalence test and in a two-sample t-test, assuming no difference as the null hypothesis.","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":" ","pages":"448-452"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Testing for normality: a user's (cautionary) guide. 正态性测试：用户（警示）指南。

IF 1.3 4区农林科学

Laboratory Animals Pub Date : 2024-10-01 DOI: 10.1177/00236772241276808

Romain-Daniel Gosselin

{"title":"Testing for normality: a user's (cautionary) guide.","authors":"Romain-Daniel Gosselin","doi":"10.1177/00236772241276808","DOIUrl":"https://doi.org/10.1177/00236772241276808","url":null,"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.","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":"58 5","pages":"433-437"},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding p-values and significance. 了解 p 值和显著性。

IF 1.3 4区农林科学

Laboratory Animals Pub Date : 2024-10-01 Epub Date: 2024-09-24 DOI: 10.1177/00236772241247106

Naomi Altman, Martin Krzywinski

引用次数: 0

Ditching the norm: Using alternative distributions for biological data analysis. 抛弃常规：使用替代分布进行生物数据分析

IF 1.3 4区农林科学

Laboratory Animals Pub Date : 2024-10-01 Epub Date: 2024-08-19 DOI: 10.1177/00236772241246602

Stanley E Lazic

引用次数: 0

Laboratory Animals presents Special Issue Biostatistics Notes. 实验动物》推出特刊《生物统计笔记》。