  We discuss a Bayesian model selection approach to high dimensional data in
the deep under sampling regime. The data is based on a representation of the
possible discrete states $s$, as defined by the observer, and it consists of
$M$ observations of the state. This approach shows that, for a given sample
size $M$, not all states observed in the sample can be distinguished. Rather,
only a partition of the sampled states $s$ can be resolved. Such partition
defines an {\em emergent} classification $q_s$ of the states that becomes finer
and finer as the sample size increases, through a process of {\em symmetry
breaking} between states. This allows us to distinguish between the
$resolution$ of a given representation of the observer defined states $s$,
which is given by the entropy of $s$, and its $relevance$ which is defined by
the entropy of the partition $q_s$. Relevance has a non-monotonic dependence on
resolution, for a given sample size. In addition, we characterise most relevant
samples and we show that they exhibit power law frequency distributions,
generally taken as signatures of "criticality". This suggests that
"criticality" reflects the relevance of a given representation of the states of
a complex system, and does not necessarily require a specific mechanism of
self-organisation to a critical point.
