Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case–control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system’s event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures—largely developed for economic and ecological research—to be useful in modern translational psychiatric science.

更广泛地说，异质性是精神病学研究和科学中的重要概念。它对病例控制范式下的效应量估计产生负面影响，并且暴露了我们现有分类学中的重要缺陷。但是，我们的领域还没有确切的异质性定义。我们倾向于通过测量相关的相关性来量化异质性，例如熵或方差：类似于接受球体半径作为其体积度量的实践。根据异质性的定义，即系统偏离完美一致性的程度，本文认为其适当的度量值大致对应于系统事件/样本空间的大小，并且具有称为数均等的单位。我们通过对生态学家对100多年（重新）发现索引的研究进行重点回顾来得出这个结论，经济学家，统计物理学家等。同时，我们回顾了用于量化异质性的精神病学方法，包括但不限于症状异质性，微生物组生物多样性，聚类计数和时间序列分析的研究。我们认为，使用数字等效的异质性度量可以改善关于异质性的精神病学研究的可解释性和综合性。但是，要在现代转化精神病学中使用这些措施（为经济和生态研究而大量开发），必须克服明显的局限性。我们认为，使用数字等效的异质性度量可以改善关于异质性的精神病学研究的可解释性和综合性。但是，要在现代转化精神病学中使用这些措施（为经济和生态研究而大量开发），必须克服明显的局限性。我们认为，使用数字等效的异质性度量可以改善关于异质性的精神病学研究的可解释性和综合性。但是，要在现代转化精神病学中使用这些措施（为经济和生态研究而大量开发），必须克服明显的局限性。