  Multivariate analyses play an important role in high energy physics. Such
analyses often involve performing an unbinned maximum likelihood fit of a
probability density function (p.d.f.) to the data. This paper explores a
variety of unbinned methods for determining the goodness of fit of the p.d.f.
to the data. The application and performance of each method is discussed in the
context of a real-life high energy physics analysis (a Dalitz-plot analysis).
Several of the methods presented in this paper can also be used for the
non-parametric determination of whether two samples originate from the same
parent p.d.f. This can be used, e.g., to determine the quality of a detector
Monte Carlo simulation without the need for a parametric expression of the
efficiency.
