标题： 当数字误导我们 显示英文标题

标题： When Numbers Mislead Us

摘要： 认为数字能对现实提供单一、客观的描述忽视了一个关键事实：数据本身不会说话。 每个数据集都是选择的结果——测量什么、如何测量、何时测量以及与谁测量——这些选择不可避免地反映了关于什么值得量化的一些隐含的，有时是意识形态的假设。 此外，在任何分析中，未被测量的内容可能与被测量的内容同样重要。 当一个关键变量被遗漏时——无论是由于疏忽、设计还是无知——它可能会扭曲其他变量之间的观察关系。 这种现象被称为遗漏变量偏差，可能导致误导性的相关性或掩盖真实的效果。 在某些情况下，考虑到这个隐藏因素可以完全推翻从表面分析中得出的结论。 这正是辛普森悖论背后的机制。 显示英文摘要

摘要： The belief that numbers offer a single, objective description of reality overlooks a crucial truth: data does not speak for itself. Every dataset results from choices-what to measure, how, when, and with whom-which inevitably reflect implicit, and sometimes ideological, assumptions about what is worth quantifying. Moreover, in any analysis, what remains unmeasured can be just as significant as what is captured. When a key variable is omitted-whether by neglect, design, or ignorance-it can distort the observed relationships between other variables. This phenomenon, known as omitted variable bias, may produce misleading correlations or conceal genuine effects. In some cases, accounting for this hidden factor can completely overturn the conclusions drawn from a superficial analysis. This is precisely the mechanism behind Simpson's paradox.