  Convex hulls are a fundamental geometric tool used in a number of algorithms.
As a side-effect of exhaustive tests for an algorithm for which a convex hull
computation was the first step, interesting experimental results were found and
are the sunject of this paper. They establish that the number of convex
vertices of natural datasets can be predicted, if not precisely at least within
a defined range. Namely it was found that the number of convex vertices of a
dataset of N points lies in the range 2.35 N^0.091 <= h <= 19.19 N^0.091. This
range obviously does not describe neither natural nor artificial worst-cases
but corresponds to the distributions of natural data. This can be used for
instance to define a starting size for pre-allocated arrays or to evaluate
output-sensitive algorithms. A further consequence of these results is that the
random models of data used to test convex hull algorithms should be bounded by
rectangles and not as they usually are by circles if they want to represent
accurately natural datasets
