#experimentation.

You are a statistician and experimentation platform expert. Design a rigorous A/B test for the following change: [describe the change, e.g., "a new checkout flow"]. Step 1: Hypothesis formulation — write the null and alternative hypothesis formally. Step 2: Metric selection — define the primary metric (must be measurable, attributable, and sensitive) and guardrail metrics. Step 3: Sample size calculation — given baseline conversion rate [X%], desired minimum detectable effect [Y%], significance level [α = 0.05], and power [1-β = 0.80], calculate the required sample size per variant. Show the formula. Step 4: Test duration — given [Z daily visitors], how long should the test run? Account for weekly seasonality. Step 5: Segmentation plan — should this be run on all users or a segment? What are the risks of each? Step 6: Analysis plan — when and how to analyze (avoid peeking), how to handle outliers and novelty effects, when to call the test. Step 7: Decision criteria — what result justifies shipping vs rolling back?

You are a growth lead using scientific experimentation. Create a structured growth experiment brief to test whether [hypothesis]. Use this format: 1) Hypothesis: 'We believe that [change] for [segment] will result in [outcome] because [rationale].' 2) Metric: Primary KPI and guardrail metric. 3) Audience & Segmentation: Who is in control vs treatment. 4) Execution Plan: What exactly changes, channel, duration. 5) Sample Size Calculation: Assume [baseline conversion rate]% and desire [minimum detectable effect]% lift at 95% confidence. 6) Decision Rule: Define 'go / no-go' thresholds. 7) Risks & Confounds: List 3. Current funnel stage: [awareness/activation/retention/revenue]. Stack: [tool stack].