Data from: On the variety of methods for calculating confidence intervals by bootstrapping

1. Researchers often want to place a confidence interval around estimated parameter values calculated from a sample. This is commonly implemented by bootstrapping. There are several different frequently used bootstrapping methods for this purpose. 2. Here we demonstrate that authors of recent papers frequently do not specify the method they have used and that different methods can produce markedly different confidence intervals for the same sample and parameter estimate. 3. We encourage authors to be more explicit about the method they use (and number of bootstrap resamples used). 4. We recommend the bias corrected and accelerated method as giving generally good performance; although researchers should be warned that coverage of bootstrap confidence intervals is characteristically less than the specified nominal level, and confidence interval evaluation by any method can be unreliable for small samples in some situations.1. 研究人员通常需要为基于样本计算得到的估计参数值构建置信区间（confidence interval），该任务常通过自助法（bootstrap）实现。目前针对该场景已有多种常用的自助法方案。 2. 本文通过实证分析表明，近期发表论文的作者往往未明确说明其所使用的自助法类型，且不同的自助法针对同一组样本与参数估计值，可生成差异显著的置信区间。 3. 我们呼吁相关作者应更清晰地说明其所采用的自助法（以及自助重采样次数）。 4. 我们推荐采用偏差校正加速法（bias corrected and accelerated method），其整体表现较为优异；但需提醒研究人员注意：自助法置信区间的实际覆盖率通常低于预设的名义水平，且在部分场景下，任何方法构建的置信区间针对小样本的评估结果均可能不可靠。