Hypothesis:
"We believe that adding social proof ('X people bought this') 
will increase conversion rate by 10% 
because it reduces purchase anxiety."

Implementation:
- Control: No social proof
- Treatment: Show "X people bought"
- Sample size: 10,000 users per variant
- Duration: 2 weeks

Test:
- Primary metric: Conversion rate
- Guardrails: Cart abandonment, return rate

Ship or Stop:
- If conversion +5% or more → Ship
- If conversion -2% or less → Stop
- If inconclusive → Iterate and retest

Feature: Streamlined checkout

Primary Metric:
✅ Purchase completion rate (+10%)

Guardrail Metrics:
⚠️ Cart abandonment (don't increase)
⚠️ Return rate (don't increase)
⚠️ Support tickets (don't increase)
⚠️ Load time (stay <2s)

Minimum sample size = (Effect size, Confidence, Power)

Typical settings:
- Confidence: 95% (p < 0.05)
- Power: 80% (detect 80% of real effects)
- Effect size: Minimum detectable change

Example:
- Baseline conversion: 10%
- Minimum detectable effect: +1% (to 11%)
- Required: ~15,000 users per variant

// Feature flag pattern
function checkoutFlow(user) {
  if (isFeatureEnabled(user, 'new-checkout')) {
    return newCheckoutExperience();
  } else {
    return oldCheckoutExperience();
  }
}

// Gradual rollout
function isFeatureEnabled(user, feature) {
  const rolloutPercent = getFeatureRollout(feature);
  const userBucket = hashUserId(user.id) % 100;
  return userBucket < rolloutPercent;
}

// Experiment assignment
function assignExperiment(user, experiment) {
  const variant = consistentHash(user.id, experiment);
  track('experiment_assigned', {
    userId: user.id,
    experiment: experiment,
    variant: variant
  });
  return variant;
}

NEW FEATURE
│
├─ Affects core metrics? ──────YES──→ EXPERIMENT REQUIRED
│  NO ↓
│
├─ Risky change? ──────────────YES──→ EXPERIMENT RECOMMENDED
│  NO ↓
│
├─ Uncertain impact? ──────────YES──→ EXPERIMENT USEFUL
│  NO ↓
│
├─ Easy to A/B test? ─────────YES──→ WHY NOT EXPERIMENT?
│  NO ↓
│
└─ SHIP WITHOUT TEST ←────────────────┘
   (But still feature flag for rollback)

# Experiment: [Name]

## Hypothesis
**We believe:** [change]
**Will cause:** [metric] to [increase/decrease]
**Because:** [reasoning]

## Variants

### Control (50%)
[Current experience]

### Treatment (50%)
[New experience]

## Metrics

### Primary Metric
- **What:** [metric name]
- **Current:** [baseline]
- **Target:** [goal]
- **Success:** [threshold]

### Guardrail Metrics
- **Metric 1:** [name] - Don't decrease
- **Metric 2:** [name] - Don't increase
- **Metric 3:** [name] - Maintain

## Sample Size
- **Users needed:** [X per variant]
- **Duration:** [Y days]
- **Confidence:** 95%
- **Power:** 80%

## Implementation
```javascript
if (experiment('feature-name') === 'treatment') {
  // New experience
} else {
  // Old experience
}

### Template 2: Feature Flag Implementation

```typescript
// features.ts
export const FEATURES = {
  'new-checkout': {
    rollout: 10,  // 10% of users
    enabled: true,
    description: 'New streamlined checkout flow'
  },
  'ai-recommendations': {
    rollout: 0,  // Not live yet
    enabled: false,
    description: 'AI-powered product recommendations'
  }
};

// feature-flags.ts
export function isEnabled(userId: string, feature: string): boolean {
  const config = FEATURES[feature];
  if (!config || !config.enabled) return false;
  
  const bucket = consistentHash(userId) % 100;
  return bucket < config.rollout;
}

// usage in code
if (isEnabled(user.id, 'new-checkout')) {
  return <NewCheckout />;
} else {
  return <OldCheckout />;
}

# Experiment Dashboard

## Active Experiments

### Experiment 1: [Name]
- **Status:** Running
- **Started:** [date]
- **Progress:** [X]% sample size reached
- **Primary metric:** [current result]
- **Guardrails:** ✅ All healthy

### Experiment 2: [Name]
- **Status:** Complete
- **Result:** Treatment won (+15% conversion)
- **Decision:** Ship to 100%
- **Shipped:** [date]

## Key Metrics

### Experiment Velocity
- **Experiments launched:** [X per month]
- **Win rate:** [Y]%
- **Average duration:** [Z] days

### Impact
- **Revenue impact:** +$[X]
- **Conversion improvement:** +[Y]%
- **User satisfaction:** +[Z] NPS

## Learnings
- [Key insight 1]
- [Key insight 2]
- [Key insight 3]

if (experiments.isEnabled('instant-payouts')) {
  return <InstantPayouts />;
}