 The standard case in the statistical literature on parsimony concerns  curve-fitting.[15]  We imagine a situation in which we have a set of discrete data points and are looking for the curve (i.e. function) which has generated them. The issue of what family of curves the answer belongs in (e.g. in LIN or in PAR) is often referred to as model-selection. The basic idea is that there are two competing criteria for model selection—parsimony and goodness of fit. The possibility of measurement error and ‘noise’ in the data means that the correct curve may not go through every data point. Indeed, if goodness of fit were the only criterion then there would be a danger of ‘overfitting’ the model to accidental discrepancies unrepresentative of the broader regularity. Parsimony acts as a counterbalance to such overfitting, since a curve passing through every data point is likely to be very convoluted and hence have many adjusted parameters.