  The fitness coefficient, introduced in this paper, results from a competition
between parametric and nonparametric density estimators within the likelihood
of the data. As illustrated on several real datasets, the fitness coefficient
generally agrees with p-values but is easier to compute and interpret. Namely,
the fitness coefficient can be interpreted as the proportion of data coming
from the parametric model. Moreover, the fitness coefficient can be used to
build a semiparamteric compromise which improves inference over the parametric
and nonparametric approaches. From a theoretical perspective, the fitness
coefficient is shown to converge in probability to one if the model is true and
to zero if the model is false. From a practical perspective, the utility of the
fitness coefficient is illustrated on real and simulated datasets.
