A/B Test Analysis Deep Dive
Take a real (or synthetic) A/B test dataset and run a full analysis — power, lift, segments, edge cases.
Pythonpandasscipy.statsJupyter or Marimo
About this project
A/B test analysis is the bread-and-butter of product data science. This project teaches the rigorous workflow: power analysis, primary + secondary metrics, segment interactions, novelty effects, and the writeup that drives a real decision. Use a public dataset (Kaggle has several) or simulate your own. The writeup is the deliverable.
Why build this in 2026?
Experimentation skills are protected from AI displacement — design and defense of A/B tests is judgement work.
What you'll ship
- Jupyter notebook with full analysis
PDF or blog post writeup
Executive summary (1 page)
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Skills you'll practice
pythonstatisticsab testing