Articles exploring a-b-testing-applied-series through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
10 articles
Learn how CUPED (Controlled Experiment Using Pre-Experiment Data) and other variance reduction techniques can cut your A/B test duration by 20-50% without sacrificing statistical rigor.
A practical guide to A/B testing across the e-commerce funnel — from category pages to checkout. Learn which metrics matter, where to focus tests, and why RPV beats conversion rate.
Learn why aggregate A/B test results hide the truth. Master segmentation analysis, understand heterogeneous treatment effects, and avoid the segment fishing trap.
Feature flags and A/B tests are complementary tools, not substitutes. Learn why rolling out a feature to 10% of users is NOT the same as running an experiment.
From Obama's $60M fundraising lift to modern campaign optimization, political A/B testing is a masterclass in high-stakes, time-constrained experimentation that every product team can learn from.
A/B testing isn't free. Learn the real costs — opportunity cost, engineering resources, decision delay — and develop the judgment to know when shipping fast beats measuring precisely.
Learn how to test pricing without the ethical and brand risks of showing different prices to different users. Test the pricing page, the framing, and the structure — not the number.
Standard A/B testing breaks when users influence each other. Learn about interference, network effects, and how platforms like LinkedIn and Uber solve experimentation at scale.
Trace A/B testing from 1835 drug trials through Claude Hopkins' coupon testing to Google running 10,000 experiments annually. Understanding history prevents you from reinventing old ideas.
Not all A/B tests use the same statistics. Learn which test to use for conversion rates, revenue, count data, and small samples — with a practical decision tree.