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When we analyze a sample of data from a wider population, we frequently try to understand the sampling distribution. Why is it useful to know and/or approximate the sampling distribution of a statistical summary? O The sampling distribution measures the extent of bias in our data-generating process. O Working with sampling distributions was not possible with legacy large-sample" inferential methods until modern computational power allowed for Monte Carlo simulation. O The sampling distribution can eliminate our uncertainty about the extent to which results based on a sample estimate may generalize to the wider population. O The sampling distribution can characterize the distribution of a numerical variable In a sample data set with respect to its symmetry and modality in the general population. O Calculating a sampling distribution is only appropriate for the analysis of data from a randomized controlled experiment. The sampling distribution is an important step in generating a numerical summary to characterize our uncertainty about the extent to which results based on a sample estimate may generalize to the wider population