We used Pmetrics to simulate a dataset that highlights the major strength of the non-parametric modeling approach in comparison to the parametric approach: freedom from constraining assumptions about the underlying probability distribution of model parameter values. This makes the method well suited for detecting unsuspected subpopulations (e.g. fast and slow metabolizers) or outliers. We simulated 50 sets of parameters for a single compartment intravenous infusion pharmacokinetic model with two parameters: elimination (Kel) and volume of distribution (Vd). From each Kel-Vd pair, Pmetrics calculated the concentrations of a theoretical drug sampled at 0, 1, 2, 3, 4, 6, 8, 12, 18, and 24 hours after the end of a single 500 mg dose infused over 0.5 hours. Random noise was added to each calculated concentration, sampled from a normal distribution with mean equal to 0 and standard deviation equal to 0.10*[concentration], i.e. a 10% coefficient of variation (CV) model. Since most modern analytic assays typically have 10% CV or less, we felt that this was a reasonable model. The “true” distribution for Kel, from which the 50 parameter sets were sampled, however, was a bimodal normal distribution, with equal weights (i.e. 0.5) and means of 0.08 and 0.32 h−1 (half lives of 8.6 and 2.2 hours, respectively), and a standard deviation of 0.032. Vd was a unimodal normal distribution, with mean 100 L and standard deviation 25. Kel and Vd were moderately correlated at −0.2. We also added a final, single outlier to the dataset, simulated from a Kel of 1 and Vd of 200 and then analyzed the entire dataset using both NPAG and IT2B. We have made the code and simulated datasets freely available for download from our website: www.lapk.org/teaching.php.