  The scaling of correlations as a function of system size provides important
hints to understand critical phenomena on a variety of systems. Its study in
biological systems offers two challenges: usually they are not of infinite
size, and in the majority of cases sizes can not be varied at will. Here we
discuss how finite-size scaling can be approximated in an experimental system
of fixed and relatively small size by computing correlations inside of a
reduced field of view of various sizes (i.e., "box-scaling"). Numerical
simulations of a neuronal network are used to verify such approximation, as
well as the ferromagnetic 2D Ising model. The numerical results support the
validity of the heuristic approach, which should be useful to characterize
relevant aspects of critical phenomena in biological systems.
