= Solution
\b[<Bootstrapping> estimates sampling uncertainty by repeating an analysis on resampled data.] In the ordinary nonparametric <bootstrap>, draw observations with replacement from the empirical sample, retaining its size, and recalculate the statistic many times. The resulting distribution can estimate a <standard error>, assess bias or form a justified <bootstrap confidence interval>. It is useful when an analytic sampling distribution is difficult to obtain.
The resampling unit must match the design: keep paired measurements together, and resample independent clusters when observations are clustered. Resampling individual ratings as if they were independent lecturers would misrepresent uncertainty. The ordinary <bootstrap> assumes the sample and chosen resampling scheme adequately represent the relevant data-generating variation; it does not repair <selection bias>, guarantee accurate inference from a tiny sample, or succeed for every statistic. Specify the interval method and computational settings, since a simple percentile interval is not universally appropriate.
Back to article page
