Experiment Tracker Expert

Tighten this experiment hypothesis: name a single primary metric, an expected effect size with rationale, a stop rule (sample size + minimum runtime), and one guardrail metric. Then list the 3 most likely confounders. Loose hypothesis: 'Adding a tooltip on the pricing page that says "Most teams pick Pro" will increase Pro-plan signups.' Context: ~12k pricing page visitors/week. Current Pro signup rate is 3.4% of visitors. We've never run an A/B test on pricing copy before.

Calculate sample size + duration for a checkout A/B test on 8% baseline conversion and a 5% relative lift target.

Result came back inconclusive. What's the decision process — extend, kill, ship anyway? Walk me through it.

Set up an experiment learning library so we stop running the same test twice and remember what didn't work.