# Posted in 2022

## Bayesian AB Testing

- 17 December 2022

I talk about the Bayesian approach to AB testing. The approach consists of 3 steps: making a hypothesis about the experiment, understanding time and data constraints, and interpreting the results after collecting the data. The advantage is that the Bayesian AB(C) test does not require p-values, corrections or bootstrap procedures, is conservative (does not exaggerate the result on small data) and is easily interpreted for business.

## Bayesian inference at scale: Running A/B tests with millions of observations

- 12 August 2022

Industry data scientists are increasingly making the shift over to using Bayesian methods. However, one often cited reason for avoiding this is because “Bayesian methods are slow.” There can be some truth to this, although this is often a non-issue unless working at serious scale.