  This paper presents a brief, semi-technical comparison of the essential
features of the frequentist and Bayesian approaches to statistical inference,
with several illustrative examples implemented in Python. The differences
between frequentism and Bayesianism fundamentally stem from differing
definitions of probability, a philosophical divide which leads to distinct
approaches to the solution of statistical problems as well as contrasting ways
of asking and answering questions about unknown parameters. After an
example-driven discussion of these differences, we briefly compare several
leading Python statistical packages which implement frequentist inference using
classical methods and Bayesian inference using Markov Chain Monte Carlo.
