统计证据何时足够强？利用假设检验为数据收集赋值
When is statistical evidence strong enough? Using hypothesis tests to value data collection
- Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将统计显著性视为立即建议与延迟建议间的选择，引入弃权值（A-value）以确定最需额外数据收集之处，并证明优先收集A值最大处的数据可提供有限样本福利保证。
AI中文摘要：
我们将统计显著性重新表述为在立即提出政策建议与推迟建议直至收集到进一步证据之间的一种选择。我们证明，在最小化最大遗憾准则下，福利最优决策对应于一个统计检验，其显著性水平取决于额外证据的成本和精度。通过反转这一规则，我们引入并推荐在传统p值旁边报告弃权值（A-value），以确定哪里最需要额外数据收集。A-value定义了在给定初始证据下，弃权（不执行）并建议进一步实验的福利盈亏平衡成本。当实验能力有限时，优先在A值最大的地方进行额外数据收集可提供有限样本福利保证。我们阐述了其对经济项目评估的意义。
英文摘要：
Policy decisions often hinge on conventional p-value thresholds, which ignore economic costs and benefits of further data collection. This paper recasts statistical significance as a choice between making an immediate policy recommendation and deferring it until further evidence is collected. The welfare-optimal decision corresponds, under minimax regret, to a statistical test whose level depends on the cost and precision of additional evidence. Inverting this rule, we introduce and recommend reporting the abstention-value (A-value) to determine where additional data collection is most needed. The A-value defines the break-even welfare cost of abstaining and recommending further experimentation given the initial evidence. Computing the A-value only requires a point estimate and its standard error, imposes no prior assumptions on policy effects, and can be converted to a monetary research budget using inputs already frequently used by regulators and funding agencies. When experimentation capacity is limited, we show that prioritizing additional data collection where A-values are the largest yields strong finite-sample guarantees. Applications to anti-poverty programs and to a medical study illustrate the practical benefits of using A-values.
