Articles exploring experiment design through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
6 articles
Explore how AI and large language models are transforming A/B test hypothesis generation by eliminating confirmation bias, surfacing non-obvious patterns in historical data, and accelerating the path from insight to experiment.
Learn what statistical power means for A/B testing, why 80% is the standard, and how underpowered tests lead to costly false negatives that cause you to miss winning changes.
Master A/B test sample size calculation including the relationship between baseline conversion rate, minimum detectable effect, and statistical power to design reliable experiments.
A strong hypothesis is the difference between an experiment that teaches you something and one that wastes traffic. Learn the three-part structure, common pitfalls, and how to connect hypotheses to your research findings.
Anchoring bias silently distorts A/B test results by making the control variant the psychological reference point against which all alternatives are judged, reshaping how users perceive value and make decisions.
How CUPED reduces variance in A/B tests, shortens time-to-decision, and cuts down on inconclusive experiments by leveraging pre-experiment data.