Dynamic models of nitrogen turnover in soil are being used increasingly in agricultural science. To be of value, a model must be thoroughly evaluated, and the expected accuracy of simulated values must be defined. Frequent field measurements are time-consuming and costly, and so models are often evaluated against only few data. However, this mismatch between the measured and simulated values can result in error statistics that are themselves subject to large errors and therefore unreliable. The dot-to-dot method quantifies the error associated with having too few measured values by interpolating linearly between measured data (hence, dot-to-dot) and evaluating the simulation at each time step using interpolated data where actual measurements are unavailable. A large error will be seen if the measured data do not capture the fluctuations predicted by the model. Other quantitative methods can be used to evaluate the performance of a model, but the dot-to-dot method can be used in conjunction with these to estimate whether or not the data are adequate for testing the model. If the performance of the model at the measured points is within the acceptable error, then the dot-to-dot method is used to judge whether there are too few points to evaluate the model's performance, and so to determine whether the evaluation of the model is valid.