AI 中文总结
该研究探讨AI时代统计学的核心逻辑，提出需通过统计依据关联数据与主张，明确统计学家的核心角色是构建、评判及维护统计依据，为AI与统计结合提供理论框架。
AI 中文摘要
人工智能（AI）可实现编程、模型拟合、可视化、模拟、文献合成及日益复杂的方法学任务自动化，但无法消除数据支撑科学主张或重要决策的逻辑条件。我们通过统计依据（statistical warrant）将这些条件形式化，其将数据与主张通过目标、观测机制、假设、程序、不确定性评估、验证准则、损失结构、治理及问责机制关联起来。若观测机制未识别出目标，无算法可在无额外信息或假设的情况下一致恢复该目标。基于此原则，我们围绕五项陈述组织论证：1.问题与目标是统计方法的核心；2.数据仅通过设计、来源及假设获得证据意义；3.描述、预测、因果推断与决策是数学上不同的任务；4.分析丰富性需考虑分析的选择方式、分析系统内的不确定性及部署验证；5.统计学家的核心角色是构建、评判及维护统计依据，包括在现有理论不足时开发新方法，该角色需统计推理及可归因的人类与机构责任。
英文摘要
Artificial intelligence (AI) can automate programming, model fitting, visualization, simulation, literature synthesis, and increasingly sophisticated methodological tasks, but it cannot remove the logical conditions under which data support scientific claims or consequential decisions. We formalize these conditions through statistical warrant, which connects data to a claim through the target, observation regime, assumptions, procedure, uncertainty assessment, validation criterion, loss structure, governance and accountability. No algorithm can consistently recover a target that is not identified by the observation regime without additional information or assumptions. From this principle, we organize the argument around five statements. Questions and targets are integral to statistical methods. Data acquire evidential meaning only through design, provenance, and assumptions. Description, prediction, causal inference, and decision are mathematically distinct tasks. Analytical abundance requires accounting for how analyses are selected, uncertainty across the analytical system, and deployment validation. The statistician's fundamental role is therefore to construct, criticize, and safeguard statistical warrant, including by developing new methodology when existing theory is inadequate. This role requires statistical reasoning and attributable human and institutional responsibility.
