  For more than $50$ years the {\it Mean Measure of Divergence} (MMD) has been
one of the most prominent tools used in anthropology for the study of
non-metric traits. However, one of the problems, in anthropology including
palaeoanthropology (more often there), is the lack of big enough samples or the
existence of samples without sufficiently measured traits. Since 1969, with the
advent of bootstrapping techniques, this issue has been tackled successfully in
many different ways. Here, we present a parametric bootstrap technique based on
the fact that the transformed $ \theta $, obtained from the Anscombe
transformation to stabilize the variance, nearly follows a normal distribution
with zero mean and variance $ \sigma^2 = 1 / (N + 1/2) $, where $ N $ is the
size of the measured trait. When the probabilistic distribution is known,
parametric procedures offer more powerful results than non-parametric ones. We
profit from knowing the probabilistic distribution of $ \theta $ to develop a
parametric bootstrapping method. We explain it carefully with mathematical
support. We give examples, both with artificial data and with real ones. Our
results show that this parametric bootstrap procedure is a powerful tool to
study samples with scarcity of data.
