Statistical inference often conflates the probability of a parameter with the probability of a hypothesis, a critical misunderstanding termed the ultimate issue error. This error is pervasive across the social, biological, and medical sciences, where null hypothesis significance testing (NHST) is mistakenly understood to be testing hypotheses rather than evaluating parameter estimates. Here, we advocate for using the Weight of Evidence (WoE) approach, which integrates quantitative data with qualitative background information for more accurate and transparent inference. Through a detailed example involving the relationship between vitamin D (25-hydroxy vitamin D) levels and COVID-19 risk, we demonstrate how WoE quantifies support for hypotheses while accounting for study design biases, power, and confounding factors. These findings emphasise the necessity of combining statistical metrics with contextual evaluation. This offers a structured framework to enhance reproducibility, reduce false interpretations, and foster robust scientific conclusions across disciplines.

翻译：统计推断常常将参数的概率与假设的概率混为一谈，这一关键误解被称为终极问题错误。该错误在社会科学、生物科学和医学科学中普遍存在，其中零假设显著性检验（NHST）被错误地理解为检验假设，而非评估参数估计值。本文主张采用证据权重法，该方法将定量数据与定性背景信息相结合，以实现更准确、更透明的推理。通过一个涉及维生素D（25-羟基维生素D）水平与COVID-19风险关系的详细示例，我们展示了证据权重法如何在考虑研究设计偏倚、统计功效和混杂因素的同时，量化对假设的支持程度。这些发现强调了将统计指标与情境评估相结合的必要性。这提供了一个结构化框架，以增强跨学科研究的可重复性、减少错误解释并促进稳健的科学结论。