Articles exploring sample-size through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
6 articles
The Statistical Debt framework shows how underpowered tests and post-hoc metrics compound silently — until one shipped false positive costs the team years of credibility.
There's no single number. But there is a rigorous framework. Here's how to calculate exactly how long your A/B test needs to run — and why stopping early is the most expensive mistake in CRO.
A practical guide to A/B test sample size calculations, with worked examples, common mistakes, and what to do when your traffic is too low. Covers multiple variations, MDE sensitivity, and how to avoid the '2-week arbitrary duration' trap.
MDE is the most underrated concept in A/B testing. Get it wrong and you'll run underpowered tests for months. This guide covers MDE calculations, real benchmarks by test type, and how to frame it as a business decision.
MDE isn't a calculator input — it's the foundation of your entire experiment design. Set it wrong and you'll either run 6-month tests or miss real effects entirely. Here's the framework, the math, and the business ROI approach that changes how you plan experiments.
Learn how to calculate the right sample size and test duration for A/B tests. Understand regression to the mean, why peeking kills tests, and the magic number myth.