A/B Testing with Python

A/B Testing with Python

Now that we've covered the statistical ideas behind experiments in A Guide to A/B Testing, we are ready to make the workflow executable.

In software terms, an A/B test is a production experiment with a data contract:

1. assign users to a control or variant group
2. collect an outcome metric
3. compare groups with a statistical test
4. decide whether the observed lift is large enough and reliable enough to ship

This lesson stays in the Patterns and Experiments section because A/B testing is not a model-training technique like regression or classification. It is a way to validate product hypotheses with data.

By the end, you will be able to analyze a simple conversion experiment in Python and explain the result clearly.