  We outline how modern likelihood theory, which provides essentially exact
inferences in a variety of parametric statistical problems, may routinely be
applied in practice. Although the likelihood procedures are based on analytical
asymptotic approximations, the focus of this paper is not on theory but on
implementation and applications. Numerical illustrations are given for logistic
regression, nonlinear models, and linear non-normal models, and we describe a
sampling approach for the third of these classes. In the case of logistic
regression, we argue that approximations are often more appropriate than
`exact' procedures, even when these exist.
