ab-test-planlisted

# /digital-marketing-pro:ab-test-plan ## Purpose Dedicated A/B test planning with a structured hypothesis framework, statistical sample size calculation, variant design, and monitoring plan. Produces a complete experiment specification with statistical rigor and clear decision criteria. ## Input Required The user must provide (or will be prompted for): - **Element to test**: The specific page, component, or experience being tested (landing page headline, CTA button, pricing page layout, email subject line, checkout flow, form design, etc.) - **Current conversion rate**: Baseline conversion rate for the metric being tested (or best estimate) - **Desired minimum detectable effect (MDE)**: The smallest improvement worth detecting. **MDE is ABSOLUTE by default** — expressed in the same units as the baseline (baseline 5.0% and you want to catch a +1.0 percentage-point lift, i.e. 5.0% → 6.0% ⇒ `--mde 0.01 --mde-type absolute`). To express it as a **relative** lift instead (a 10% relative improvement on a 5% baseline = 5.5% ⇒ `--mde 0.10 --mde-type relative`), pass `--mde-type relative`. This distinction is the single most common sample-size error: the same "10%" read as absolute vs. relative changes the required sample size by roughly two orders of magnitude (~200×) at a 5% baseline. Always confirm which the user means. - **Daily traffic or impressions**: Average daily visitors or impressions to the test page or element - **Significance level**: Desired confidence level, defau