  Modeling and simulation are recognized as important aspects of the scientific
method for more than 70 years but its adoption in biology has been slow.
Debates on its representativeness, usefulness, and whether the effort spent on
such endeavors is worthwhile, exist to this day. Here, I argue that most of
learning is modeling; hence, arriving at a contradiction if models are not
useful. Representing biological systems through mathematical models can be
difficult but the modeling procedure is a process in itself that follows a
semi-formal set of rules. Although seldom reported, failure in modeling is not
a rare event but I argue that this is usually a result of erroneous underlying
knowledge or misapplication of a model beyond its intended purpose. I argue
that in many biological studies, simulation is the only experimental tool. In
others, simulation is a means of reducing possible combinations of experimental
work; thereby, presenting an economical case for simulation; thus, worthwhile
to engage in this endeavor. The representativeness of simulation depends on the
validation, verification, assumptions, and limitations of the underlying model.
This will be illustrated using the inter-relationship between population,
samples, probability theory, and statistics.
