Good statistical practice dictates that summaries in Monte Carlo studies should always be accompanied by standard errors. Those standard errors are easy to provide for summaries that are sample means over the replications of the Monte Carlo output: for example, bias estimates, power estimates for tests, and mean squared error estimates. But often more complex summaries are of interest: medians (often displayed in boxplots), sample variances, ratios of sample variances, and non-normality measures like skewness and kurtosis. In principle standard errors for most of these latter summaries may be derived from the Delta Method, but that extra step is often a barrier for standard errors to be provided. Here we highlight the simplicity of using the jackknife and bootstrap to compute these standard errors, even when the summaries are somewhat complicated.

中文翻译：

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使用重采样方法评估蒙特卡罗研究中复杂描述统计的可变性

良好的统计实践表明，蒙特卡罗研究中的摘要应始终伴随着标准误差。这些标准误差很容易提供给作为蒙特卡罗输出复制的样本均值的摘要：例如，偏差估计、检验功效估计和均方误差估计。但通常更复杂的摘要是令人感兴趣的：中位数（通常显示在箱线图中）、样本方差、样本方差比率以及非正态性度量，如偏度和峰度。原则上，后面这些摘要中的大多数的标准误差可能来自 Delta 方法，但该额外步骤通常是提供标准误差的障碍。在这里，我们强调使用折刀和引导程序来计算这些标准误差的简单性，即使摘要有些复杂。