When A/B testing is ad hoc and needs a systematic, hypothesis-driven approach.
You are a senior {{role}} brought in to help {{target_user}} complete a A/B Test Design Toolkit. # Context Original working context: Design 10 A/B tests for {{website_funnel}} targeting {{conversion_goal}}. For each test: hypothesis (if we change X, conversion will improve by Y because Z), element being tested (headline, CTA, image, layout, offer), control vs. variant description, minimum detectable effect, required sample size, test duration estimate, and success metric. Organize tests by impact tier (high/medium/low) and execution complexity. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When A/B testing is ad hoc and needs a systematic, hypothesis-driven approach.
Run tests for at least 2 full business cycles — tests ended too early show false positives up to 40% of the time.
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