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 Q2 VETERINARY SCIENCES

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

Stanley E Lazic

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引用次数: 0

摘要

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

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.

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来源期刊

Laboratory Animals 生物-动物学

CiteScore

4.90

自引率

8.30%

发文量

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.