数据越多越可靠？这项研究揭示了反例：错误的数据会放大结论偏差。
Stable but Wrong: When More Data Degrades Scientific Conclusions
- 在观测可靠性不可见时，标准推断会稳定收敛却得出错误结论
- 增加数据量反而放大错误，诊断指标仍显示正常
- 适合关注科学推断可信性、数据质量的科研人员
现代科学越来越依赖不断增长的观测数据集和自动化推断流程，隐含假设是数据越多，结论越可靠。本文揭示这一假设可能在根本上失效。我们识别出一种结构化情形：标准推断过程平稳收敛、校准良好且通过常规诊断检验，却系统性地趋向错误结论。这种失败源于观测可靠性以推断过程无法察觉的方式退化。通过最小合成实验，我们证明在此情形下，增加数据不仅不能纠正误差，反而加剧偏差，而残差与拟合优度诊断仍看似正常。结果表明，数据驱动科学存在内在局限：稳定性、收敛性和置信度不足以保证认识论有效性。我们主张，推断不能视为数据可得性的无条件结果，必须对观测过程完整性施加显式约束。
原文摘要 · Abstract (English)
Modern science increasingly relies on ever-growing observational datasets and automated inference pipelines, under the implicit belief that accumulating more data makes scientific conclusions more reliable. Here we show that this belief can fail in a fundamental and irreversible way. We identify a structural regime in which standard inference procedures converge smoothly, remain well calibrated, and pass conventional diagnostic checks, yet systematically converge to incorrect conclusions. This failure arises when the reliability of observations degrades in a manner that is intrinsically unobservable to the inference process itself. Using minimal synthetic experiments, we demonstrate that in this regime additional data do not correct error but instead amplify it, while residual-based and goodness-of-fit diagnostics remain misleadingly normal. These results reveal an intrinsic limit of data-driven science: stability, convergence, and confidence are not sufficient indicators of epistemic validity. We argue that inference cannot be treated as an unconditional consequence of data availability, but must instead be governed by explicit constraints on the integrity of the observational process.
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