Ditching the norm: Using alternative distributions for biological data analysis.

IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES
Laboratory Animals Pub Date : 2024-10-01 Epub Date: 2024-08-19 DOI:10.1177/00236772241246602
Stanley E Lazic
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引用次数: 0

Abstract

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for.

抛弃常规:使用替代分布进行生物数据分析
大多数经典统计检验都假设数据呈正态分布。如果不符合这一假设,研究人员通常会求助于非参数方法。这些方法有一些缺点,如果没有合适的非参数检验方法,可能会不恰当地使用正态分布。更好的选择是从现代软件包中的几十种分布中选择适合数据的分布。选择一个能代表数据生成过程的分布是分析数据的关键步骤,但却被忽视了。本文将讨论几种可供选择的分布及其适用的数据类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Laboratory Animals
Laboratory Animals 生物-动物学
CiteScore
4.90
自引率
8.30%
发文量
64
审稿时长
6-12 weeks
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.
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