  One way of revealing the nature of the coronal heating mechanism is by
comparing simple theoretical one dimensional hydrostatic loop models with
observations at the temperature and/or density structure along these features.
The most well-known method for dealing with comparisons like that is the
$\chi^2$ approach. In this paper we consider the restrictions imposed by this
approach and present an alternative way for making model comparisons using
Bayesian statistics. In order to quantify our beliefs we use Bayes factors and
information criteria such as AIC and BIC. Three simulated datasets are analyzed
in order to validate the procedure and assess the effects of varying error bar
size. Another two datasets (Ugarte-Urra et al., 2005; Priest et al., 2000) are
re-analyzed using the method described above. In one of these two datasets
(Ugarte-Urra et al., 2005), due to the error estimates in the observed
temperature values, it is not posible to distinguish between the different
heating mechanisms. For this we suggest that both Classical and Bayesian
statistics should be applied in order to make safe assumptions about the nature
of the coronal heating mechanisms.
