More realistic is the application of bootstrapping to the regression model. But there may be reason to believe that this may not necessarily be the case. Mooney and Duval provide a well illustrated example of the application of bootstrapping to such a case although at this stage some demands are made upon our understanding of statistical theory. Following this, the bootstrap is studies with the aid of Monte Carlo simulation. This term will possibly be somewhat more familiar to social scientists, but is nevertheless adequately explained.
Confidence intervals are an integral part of parametric statistics.
With a given distribution, these are relatively easily calculated. They are no less important in bootstrapping but much more complex to estimate. However, a large part of the text is devoted to this problem. This is important as bootstrapping is largely concerned with inferential statistics rather than point estimators of parameters. The fact that bootstrapping is little used must surely be a consequence of computer restraints.