 For linear models it is easy to see the intuitive appeal of such piecemeal strategies. After all, for linear systems of equations a small change in the magnitude of a variable is guaranteed to yield a proportional change in the output of the model. So by making piecemeal refinements to the initial data or to the linear model only proportional changes in model output are expected. If the linear model is faithful, then making small improvements “in the right direction” in either the initial data or the model itself can be tracked by improved model performance. The qualifier “in the right direction,” drawing upon the faithful model assumption, means that the data quality really is increased or that the model really is more realistic (captures more features of the target system in an increasingly accurate way), and is signified by the model’s monotonically improved performance with respect to the target system. 