ads-testlisted

# A/B Test Design & Experiment Planning <!-- Created: 2026-04-13 | v1.5 --> <!-- Source: OpenClaudia/openclaudia-skills (ab-test-setup concept) --> ## Process 1. Understand what the user wants to test (creative, audience, bidding, landing page) 2. Build structured hypothesis using the framework below 3. Calculate required sample size and estimated duration 4. Recommend platform-specific test setup 5. Define success criteria and measurement plan ## Hypothesis Framework Every test must start with a structured hypothesis: ``` IF we [change/action] THEN [metric] will [increase/decrease] by [estimated %] BECAUSE [reasoning based on data or insight] Example: IF we replace polished product shots with UGC creator videos THEN Meta CTR will increase by 25-40% BECAUSE Andromeda prioritizes diverse creative formats and UGC consistently outperforms polished in 2025-2026 benchmarks ``` ### Hypothesis Quality Checklist - [ ] Single variable being tested (isolate the change) - [ ] Specific metric defined (not "performance") - [ ] Estimated effect size stated (needed for sample size calculation) - [ ] Timeframe defined - [ ] Success/failure criteria clear before launch ## Statistical Significance Calculator ``` Required Sample Size (per variant): n = (Z_alpha + Z_beta)^2 × 2 × p × (1-p) / MDE^2 Where: - Z_alpha = 1.96 (for 95% confidence) - Z_beta = 0.84 (for 80% power) - p = baseline conversion rate - MDE = minimum detectable effect (relative %) Simplified lookup: ``` | Baselin