  We introduce PPBoot: a bootstrap-based method for prediction-powered
inference. PPBoot is applicable to arbitrary estimation problems and is very
simple to implement, essentially only requiring one application of the
bootstrap. Through a series of examples, we demonstrate that PPBoot often
performs nearly identically to (and sometimes better than) the earlier PPI(++)
method based on asymptotic normality$\unicode{x2013}$when the latter is
applicable$\unicode{x2013}$without requiring any asymptotic characterizations.
Given its versatility, PPBoot could simplify and expand the scope of
application of prediction-powered inference to problems where central limit
theorems are hard to prove.
