Are the highest sample realizations selected from a larger presample more or less informative than the same amount of random data? Developing multivariate accuracy for interval dominance ordered preferences, we show that sample selection always benefits (or always harms) a decision maker if the reverse hazard rate of the data distribution is log‐supermodular (or log‐submodular), as in location experiments with normal noise. We find nonpathological conditions under which the information contained in the winning bids of a symmetric auction decreases in the number of bidders. Exploiting extreme value theory, we quantify the limit amount of information revealed when the presample size (number of bidders) goes to infinity. In a model of equilibrium persuasion with costly information, we derive implications for the optimal design of selected experiments when selection is made by an examinee, a biased researcher, or contending sides with the peremptory challenge right to eliminate a number of jurors.

中文翻译：

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战略样本选择

与相同数量的随机数据相比，从较大的预样本中选择的最高样本实现量或多或少地提供了很多信息？通过为区间优势有序偏好开发多元精度，我们证明，如果数据分布的反向风险率是对数超模（或对数次模），则样本选择总是对决策者有利（或总是有害），如正态分布的位置实验中那样噪音。我们发现在非病理性条件下，对称拍卖中标中包含的信息会减少投标人的数量。利用极值理论，我们量化了当预抽样规模（投标人数量）达到无穷大时所揭示的信息的极限量。在平衡的说服模型中，信息昂贵，