  A new method for calculation of goodness of multidimensional fits in particle
physics experiments is proposed. This method finds the smallest and largest
clusters of nearest neighbors for observed data points. The cluster size is
used to estimate the goodness-of-fit and the cluster location provides clues
about possible problems with data modeling. The performance of the new method
is compared to that of the likelihood method and Kolmogorov-Smirnov test using
toy Monte Carlo studies. The new method is applied to estimate the
goodness-of-fit in a B->Kll analysis at BaBar.
