Connecting Simple and Precise P-values to Complex and Ambiguous Realities
将简洁精确的 p 值与复杂含混的现实联系起来AI 解读 ↓
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将简洁精确的 p 值与复杂含混的现实联系起来
- 研究问题
- 当数据生成、管理、分析与报告包含不可靠的隐含假设时，p 值、置信区间与后验概率应如何获得可信的现实解释？
- 方法与思路
- 作者从统计解释与研究实践的角度审视隐含假设，主张以明确的因果生成叙述及公开的机制记录支持统计量解释。
- 材料陈述的贡献
- 摘要强调统计量不能脱离数据生成背景解释，并批评把显著性与置信术语直接等同于结果重要性或可信程度的做法。
- 阅读关注点
- 应核对作者对统计量解释的具体限定、随机化等生成机制所起的作用，以及论证中的形式结论与规范性主张。
- 阅读边界
- 所据为 arXiv 预印本摘要，同行评审与期刊接收未核实；摘要主要呈现方法论立场，未提供具体定理或实证检验。
原文摘要
Mathematics is a limited component of solutions to real-world problems, as it expresses only what is expected to be true if all our assumptions are correct, including implicit assumptions that are omnipresent and often incorrect. Statistical methods are rife with implicit assumptions whose violation can be life-threatening when results from them are used to set policy. Among them are that there is human equipoise or unbiasedness in data generation, management, analysis, and reporting. These assumptions correspond to levels of cooperation, competence, neutrality, and integrity that are absent more often than we would like to believe. Given this harsh reality, we should ask what meaning, if any, we can assign to the P-values, 'statistical significance' declarations, 'confidence' intervals, and posterior probabilities that are used to decide what and how to present (or spin) discussions of analyzed data. By themselves, P-values and CI do not test any hypothesis, nor do they measure the significance of results or the confidence we should have in them. The sense otherwise is an ongoing cultural error perpetuated by large segments of the statistical and research community via misleading terminology. So-called 'inferential' statistics can only become contextually interpretable when derived explicitly from causal stories about the real data generator (such as randomization), and can only become reliable when those stories are based on valid and public documentation of the physical mechanisms that generated the data. Absent these assurances, traditional interpretations of statistical results become pernicious fictions that need to be replaced by far more circumspect descriptions of data and model relations.