Quantifying Uncertainty: All We Need is the Bootstrap?

Standard errors, confidence intervals, hypothesis tests, and other quantifications of uncertainty are essential to statistical practice. However, they feature a plethora of different methods, mathematical formulas, and concepts. Could we not just replace them all with the general and relatively easy-to-understand non-parametric bootstrap? We contribute to answering this question with a review of related work and a simulation study of one- and two-sided confidence intervals over several different sample sizes, confidence levels, data generating processes, and functionals. Results show that double bootstrap is the overall best method and a viable alternative to typically used approaches in all but the smallest sample sizes.