  The correct use and interpretation of models depends on several steps, two of
which being the calibration by parameter estimation and the analysis of
uncertainty. In the biological literature, these steps are seldom discussed
together, but they can be seen as fitting pieces of the same puzzle. In
particular, analytical procedures for uncertainty estimation may be masking a
high degree of uncertainty coming from a model with a stable structure, but
insufficient data.
  Under a likelihoodist approach, the problem of uncertainty estimation is
closely related to the problem of composite hypothesis. In this paper, we
present a brief historical background on the statistical school of
Likelihoodism, and examine the complex relations between the law of likelihood
and the problem of composite hypothesis, together with the existing proposals
for coping with it. Then, we propose a new integrative methodology for the
uncertainty estimation of models using the information in the collected data.
We argue that this methodology is intuitively appealing under a likelihood
paradigm.
