/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, default 95% (alpha = 0.05)
• Statistical power: Desired power, default 80% (beta = 0.20)
• Number of variants: How many variants to test (default 1 treatment + 1 control; more for multivariate)
• Business context: What prompted the test idea (analytics data, user feedback, competitive analysis, heuristic audit, stakeholder request)