Blog
Notes on A/B testing methodology and experimentation.
- CUPED Explained: How to Cut A/B Test Sample Size by 30–50% Using Pre-Experiment Data
May 11, 2026
CUPED uses each user's pre-experiment behavior to subtract predictable noise from your metric. Done right, it shrinks required sample size by 30–50% with no extra traffic, no bias, and no extra weeks on the calendar. Here's the intuition, the math, the numbers, and the four ways teams get it wrong.
- A/B Test Sample Size Calculator: How to Plan a Test That Can Actually Win
May 6, 2026
Most A/B tests fail before they start because the sample size was wrong. Here's how to plan one that has a real chance of detecting the lift you care about — with the math, the trade-offs, and a calculator.
- Minimum Detectable Effect (MDE): The One Number That Decides If Your Test Is Worth Running
May 6, 2026
MDE is the smallest lift your test can reliably detect. Pick it too small and your test runs forever; pick it too big and you'll miss real wins. Here's how to choose it like a grown-up.