  We are entering an era where progress in cosmology is driven by data, and
alternative models will have to be compared and ruled out according to some
consistent criterium. The most conservative and widely used approach is
Bayesian model comparison. In this paper we explicitly calculate the Bayes
factors for all models that are linear with respect to their parameters. We do
this in order to test the so called Jeffreys' scale and determine analytically
how accurate its predictions are in a simple case where we fully understand and
can calculate everything analytically. We also discuss the case of nested
models, e.g. one with $M_1$ and another with $M_2\supset M_1$ parameters and we
derive analytic expressions for both the Bayes factor and the Figure of Merit,
defined as the inverse area of the model parameter's confidence contours. With
all this machinery and the use of an explicit example we demonstrate that the
threshold nature of Jeffreys' scale is not a "one size fits all" reliable tool
for model comparison and that it may lead to biased conclusions. Furthermore,
we discuss the importance of choosing the right basis in the context of models
that are linear with respect to their parameters and how that basis affects the
parameter estimation and the derived constraints.
