Frequentism and Bayesianism: A Practical Introduction by Jake Vanderplas.
From the post:
One of the first things a scientist hears about statistics is that there is are two different approaches: frequentism and Bayesianism. Despite their importance, many scientific researchers never have opportunity to learn the distinctions between them and the different practical approaches that result. The purpose of this post is to synthesize the philosophical and pragmatic aspects of the frequentist and Bayesian approaches, so that scientists like myself might be better prepared to understand the types of data analysis people do.
I’ll start by addressing the philosophical distinctions between the views, and from there move to discussion of how these ideas are applied in practice, with some Python code snippets demonstrating the difference between the approaches.
This is the first of four posts that include Python code to demonstrate the impact of your starting position.
The other posts are:
- Frequentism and Bayesianism II: When Results Differ
- Frequentism and Bayesianism III: Confidence, Credibility, and why Frequentism and Science do not Mix
- Frequentism and Bayesianism IV: How to be a Bayesian in Python
Very well written and highly entertaining!
Jake leaves out another approach to statistics: Lying.
Lying avoids the need for a philosophical position or to have data for processing with Python or any other programming language. Even calculations can be lied about.
Most commonly found political campaigns, legislative hearings and the like. How you would characterize any particular political lie is left as an exercise for the reader. 😉