  A common problem in physics is to fit regression data by a parametric class
of functions, and to decide whether a certain functional form allows for a good
fit of the data. Common goodness of fit methods are based on the calculation of
the distribution of certain statistical quantities under the assumption that
the model under consideration {\it holds true}. This proceeding bears
methodological flaws, e.g. a good ``fit'' - albeit the model is wrong - might
be due to over-fitting, or to the fact that the chosen statistical criterion is
not powerful enough against the present particular deviation between model and
true regression function. This causes particular difficulties when models with
different numbers of parameters are to be compared. Therefore the number of
parameters is often penalised additionally. We provide a methodology which
circumvents these problems to some extent. It is based on the consideration of
the error distribution of the goodness of fit criterion under a broad range of
possible models - and not only under the assumption that a given model holds
true. We present a graphical method to decide for the most evident model from a
range of parametric models of the data. The method allows to quantify
statistical evidence {\it for} the model (up to some distance between model and
true regression function) and not only {\it absence of evidence} against, as
common goodness of fit methods do. Finally we apply our method to the problem
of recovering the luminosity density of the Milky Way from a de-reddened {\it
COBE/DIRBE} L-band map. We present statistical evidence for flaring of the
stellar disc inside the solar circle.
