In reply to Bradford.

Although the process is similar, "bootstrap" refers to estimating the distribution of data for real data by resampling from the data. There is no model, just the data. You use the bootstrap distribution of a statistic to estimate the true sampling distribution.

In contrast, a "simulation" assumes a population distribution from which you can repeatedly draw random samples. Each sample is independent of the others. You can explore the sampling distribution of a statistic to arbitrary closeness by taking many samples. You can compare estimates to the true parameters because you know the true parameters. To learn more about simulating confidence intervals, see "Coverage probability of confidence intervals" and "Using simulation to estimate the power of a statistical test."