experiment-decisionlisted

# Experiment Decision Framework: When to A/B Test vs Ship ## Quick Start ``` /experiment-decision ``` Then provide: 1. **What you're considering building** (feature, change, or experiment) 2. **Expected impact** (metric + estimated improvement) 3. **Your concern** (is this risky? reversible? controversial?) I'll walk you through the decision tree: reversibility, hypothesis strength, detectable impact, and risk level. You'll get a clear recommendation: A/B test, ship + monitor, or just ship. **Output:** Decision documented inline or saved to `outputs/decisions/` **Time:** ~5 min for clear-cut cases, ~15 min for nuanced decisions **When to use:** Before building any feature, when stakeholders demand "data-driven" decisions, or when unsure if testing is worth the effort **Framework source:** Aakash Gupta's "When to A/B Test vs Just Ship" --- ## The Decision Framework Use this decision tree: ### Question 1: Is it reversible? **If YES → Ship it** - CSS changes - Messaging tweaks - UI polish - Non-destructive features **Why:** Reversible changes have low risk. Ship, monitor, rollback if needed. **If NO → Continue to Question 2** --- ### Question 2: Do you have a hypothesis with measurable impact? **If NO → Don't test** - Building "nice to haves" - No clear success metric - Can't measure the outcome **Why:** Testing without a hypothesis is wasteful. Either clarify the hypothesis or don't build it. **If YES → Continue to Question 3** --- ### Question 3: Is the exp