Prioris.ai Inc., Ottawa, Canada.
Lab Anim. 2024 Oct;58(5):438-442. doi: 10.1177/00236772241246602. Epub 2024 Aug 19.
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.
大多数经典的统计检验都假设数据呈正态分布。如果这一假设不成立，研究人员通常会转而使用非参数方法。这些方法有一些缺点，如果没有合适的非参数检验，那么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提供的数十种可用分布中选择适合数据的分布。选择代表数据生成过程的分布是分析数据时一个关键但被忽视的步骤。本文讨论了几种替代分布以及它们适合的数据类型。
