Free Lesson
Experimentation 101: Run an A/B Test with AI
Part of Build Your AI Product Analyst
60 min
Dec 23, 2026 1:00 PM
What you'll learn
Frame a testable hypothesis
Turn a vague "let's try this" into a clear statement of what changes, for whom, and what metric should move.
Size the test so the result means something
Work out how many users and how long you need before you start, so you are not reading noise.
Read the result without fooling yourself
Spot false positives, peeking, and stat-sig traps, and know when a lift is real enough to trust.
Decide ship, hold, or roll back
Translate the numbers into a clear call, with the tradeoffs and risks spelled out.
Why this topic matters
Most experiment readouts ship bad decisions: p-values without validity checks, no segment analysis, vague recommendations. AI makes it easier to crunch numbers, which makes it easier to crunch the wrong ones. Learn the Agentic Experiment Loop to design, validate, and analyze tests, then see how AI agents accelerate every stage without replacing judgment.
