KinTek Global Kinetic Explorer software offers several advantages in fitting enzyme kinetic data. Behind the intuitive graphical user interface lies fast and efficient algorithms to perform numerical integration of rate equations so that kinetic parameters or starting concentrations can be scrolled while the time dependence of the reaction is dynamically updated in the graphical display. This immediate feedback between the model and the output provides a powerful tool for learning kinetics, for exploring the complex relationships between rate constants and the observable signals, and for fitting data. Dynamic simulation provides an easy means to obtain starting estimates for kinetic parameters before fitting by nonlinear regression and for exploring parameter space after a fit is achieved. Moreover, the fast algorithms for numerical integration allow for the brute force computation of confidence contours to provide reliable estimates of the range over which parameters can vary, which is especially important because it reveals when parameters are not well constrained. As illustrated by several examples outlined here, standard nonlinear regression methods fail to detect when parameters are not constrained by the data and generally produce standard error estimates that are extremely misleading. This brings forth an important distinction between a "good" fit where a minimum chi(2) is achieved and one where all variable parameters are well constrained based upon sufficient information content of the data. These concepts are illustrated by example in fitting full progress curve kinetics and in fitting the time dependence of slow-onset inhibition.