异常值的影响：基于后果的检测
Outlier Impact: Detection by Consequences
AI总结：
该研究提出一种基于异常值统计具体影响的检测方法，可标记出影响数据推断且异常的点，尤其关注其对假设检验均值和FPR的影响，适用于需保证因果推断有效性的实验场景。
AI中文摘要：
我们提出一种新颖的异常值检测方法，该方法利用异常值对统计的具体影响程度，而非模糊的“异常”概念。这使得从业者仅标记出那些会实质性影响数据推断且确实异常的点，这对实验尤为重要，因为异常值会影响因果推断的有效性。我们特别考虑潜在异常值对假设检验的均值和假阳性率（FPR）的影响。
英文摘要：
We introduce a novel outlier detection method that utilizes a concrete measure of the statistical impact of an outlier rather than a vague notion of "unusualness". This allows practitioners to only flag points that materially affect the inferences made from the data while also ensuring these points are anomalous. This is of particular interest in experimentation, where outliers affect the validity of the causal inferences. In particular, we consider a potential outlier's impact on the mean and the False Positive Rate (FPR) of a hypothesis test.
