  Many aspects of the evolution of stars, and in particular the evolution of
binary stars, remain beyond our ability to model them in detail. Instead, we
rely on observations to guide our often phenomenological models and pin down
uncertain model parameters. To do this statistically requires population
synthesis. Populations of stars modelled on computers are compared to
populations of stars observed with our best telescopes. The closest match
between observations and models provides insight into unknown model parameters
and hence the underlying astrophysics. In this brief review, we describe the
impact that modern big-data surveys will have on population synthesis, the
large parameter space problem that is rife for the application of modern data
science algorithms, and some examples of how population synthesis is relevant
to modern astrophysics.
