Testing whole sequences:
- Email 1 + Email 2 + Email 3 = Sequence A
- Need hundreds of sends per variant
- Slow to reach significance
- Don't know which message made the difference

Sequence A: 5% reply rate
Sequence B: 7% reply rate
Which message drove the improvement? Unknown.

Testing individual messages:
- Test Email 1 variants across all sequences
- Reach significance faster
- Know exactly what works
- Compound improvements

Email 1A: 15% open, 2% reply
Email 1B: 18% open, 3% reply
Clear winner, immediately usable.

Test variants:
A: "Quick question about [Company]"
B: "[Name], question about [pain point]"
C: "Saw your post on [topic]"

Metrics:
- Open rate (primary)
- Reply rate (secondary)
- Spam rate (guardrail)

Sample size: 50-100 per variant minimum

Test variants:
A: "I noticed [Company] just [event]..."
B: "Fellow [industry] professional here..."
C: "Not sure if this is relevant, but..."

Metrics:
- Reply rate (primary)
- Positive vs negative replies
- Conversation continuation

Test variants:
A: "Worth a 15-minute call?"
B: "Open to learning more?"
C: "What's the best way to continue this?"
D: "Reply with 'yes' if interested"

Metrics:
- Reply rate
- Meeting conversion
- Quality of responses

Test variants:
A: Formal professional
B: Casual conversational
C: Direct and brief
D: Storytelling approach

Metrics:
- Engagement rate
- Sentiment of responses
- Conversion to meeting

class MessageABTest:
    def __init__(self, test_config):
        self.test_id = generate_id()
        self.variants = test_config.variants
        self.metrics = test_config.metrics
        self.traffic_split = test_config.split  # e.g., [0.5, 0.5]
        self.min_sample = test_config.min_sample
        self.status = "running"
        self.results = {v.id: {"sent": 0, "results": {}} for v in self.variants}

    def select_variant(self, context):
        if self.should_use_winner():
            return self.get_winner()

        # Random assignment with traffic split
        rand = random.random()
        cumulative = 0
        for i, split in enumerate(self.traffic_split):
            cumulative += split
            if rand < cumulative:
                return self.variants[i]

    def record_result(self, variant_id, metric, value):
        self.results[variant_id]["results"].setdefault(metric, []).append(value)
        self.results[variant_id]["sent"] += 1
        self.check_significance()

    def check_significance(self):
        if all(v["sent"] >= self.min_sample for v in self.results.values()):
            winner = self.calculate_winner()
            if winner and winner["confidence"] >= 0.95:
                self.status = "completed"
                self.winner = winner

def select_message_variant(message_type, context):
    # Find active tests for this message type
    active_tests = get_active_tests(message_type)

    if not active_tests:
        return get_default_message(message_type, context)

    # Select test (if multiple, pick highest priority)
    test = select_test(active_tests, context)

    # Get variant assignment
    variant = test.select_variant(context)

    # Log assignment for tracking
    log_test_assignment(
        test_id=test.test_id,
        variant_id=variant.id,
        context_id=context.conversation_id
    )

    return variant.content

def track_message_result(message_id, event):
    # Get test assignment
    assignment = get_test_assignment(message_id)
    if not assignment:
        return

    test = get_test(assignment.test_id)

    # Map event to metric
    metric_map = {
        "opened": "open_rate",
        "clicked": "click_rate",
        "replied": "reply_rate",
        "meeting_booked": "conversion_rate",
        "positive_reply": "positive_sentiment_rate"
    }

    metric = metric_map.get(event.type)
    if metric:
        test.record_result(
            variant_id=assignment.variant_id,
            metric=metric,
            value=event.value
        )

def calculate_sample_size(baseline_rate, min_detectable_effect, power=0.8, alpha=0.05):
    """
    baseline_rate: Current conversion rate (e.g., 0.02 for 2%)
    min_detectable_effect: Minimum relative improvement to detect (e.g., 0.2 for 20%)
    """
    from scipy import stats

    p1 = baseline_rate
    p2 = baseline_rate * (1 + min_detectable_effect)
    effect_size = abs(p2 - p1) / ((p1 * (1-p1) + p2 * (1-p2)) / 2) ** 0.5

    z_alpha = stats.norm.ppf(1 - alpha/2)
    z_beta = stats.norm.ppf(power)

    n = 2 * ((z_alpha + z_beta) / effect_size) ** 2
    return int(n)

undefined

def test_significance(variant_a_results, variant_b_results, metric):
    from scipy import stats

    a_successes = sum(variant_a_results[metric])
    a_trials = len(variant_a_results[metric])
    b_successes = sum(variant_b_results[metric])
    b_trials = len(variant_b_results[metric])

    # Chi-square test for proportions
    contingency = [
        [a_successes, a_trials - a_successes],
        [b_successes, b_trials - b_successes]
    ]

    chi2, p_value, dof, expected = stats.chi2_contingency(contingency)

    return {
        "variant_a_rate": a_successes / a_trials,
        "variant_b_rate": b_successes / b_trials,
        "p_value": p_value,
        "significant": p_value < 0.05,
        "winner": "A" if a_successes/a_trials > b_successes/b_trials else "B"
    }

class ThompsonSampling:
    """
    Balances exploration vs exploitation during testing.
    Automatically shifts traffic to winning variants.
    """
    def __init__(self, variants):
        self.variants = variants
        self.successes = {v: 1 for v in variants}  # Prior
        self.failures = {v: 1 for v in variants}   # Prior

    def select_variant(self):
        samples = {}
        for variant in self.variants:
            # Sample from beta distribution
            samples[variant] = random.betavariate(
                self.successes[variant],
                self.failures[variant]
            )
        return max(samples, key=samples.get)

    def update(self, variant, success):
        if success:
            self.successes[variant] += 1
        else:
            self.failures[variant] += 1

1. Hypothesis: "Personalized subject lines increase open rates"
2. Design: Create variants A and B
3. Configure: Set metrics, sample size, traffic split
4. Launch: Begin test
5. Monitor: Track interim results
6. Analyze: Check for significance
7. Conclude: Declare winner or inconclusive
8. Deploy: Roll out winner, archive loser

def can_run_test(new_test, active_tests):
    """Prevent test interference"""

    for test in active_tests:
        # Same message position = conflict
        if test.message_position == new_test.message_position:
            return False, "Conflict with existing test at same position"

        # Same audience segment = potential conflict
        if overlaps(test.audience, new_test.audience) > 0.5:
            return False, "Audience overlap >50% with active test"

    return True, None

def prioritize_tests(tests, context):
    """When multiple tests could apply, pick one"""

    eligible = [t for t in tests if t.matches_context(context)]

    if not eligible:
        return None

    # Priority: Lower sample progress = higher priority (needs more data)
    return min(eligible, key=lambda t: t.sample_progress)

def generate_test_report(test_id):
    test = get_test(test_id)

    return {
        "test_info": {
            "id": test.test_id,
            "hypothesis": test.hypothesis,
            "start_date": test.started_at,
            "status": test.status
        },
        "variants": [
            {
                "id": v.id,
                "description": v.description,
                "sent": test.results[v.id]["sent"],
                "metrics": calculate_metrics(test.results[v.id])
            }
            for v in test.variants
        ],
        "significance": {
            "is_significant": test.is_significant,
            "p_value": test.p_value,
            "confidence": test.confidence,
            "winner": test.winner
        },
        "recommendation": generate_recommendation(test)
    }

def generate_insights(completed_tests):
    insights = []

    # Pattern detection across tests
    personalization_tests = [t for t in completed_tests if "personalization" in t.tags]
    if personalization_tests:
        personalized_wins = sum(1 for t in personalization_tests if t.personalized_won)
        insights.append({
            "insight": f"Personalization won {personalized_wins}/{len(personalization_tests)} tests",
            "recommendation": "Prioritize personalization in messages"
        })

    # Identify winning patterns
    winning_variants = [t.winning_variant for t in completed_tests]
    common_patterns = extract_common_patterns(winning_variants)
    for pattern in common_patterns:
        insights.append({
            "insight": f"Pattern '{pattern}' appears in {pattern.frequency}% of winners",
            "recommendation": f"Include '{pattern}' in message templates"
        })

    return insights

1. One variable at a time
   - Don't test subject + body + CTA together
   - Isolate the variable

2. Meaningful differences
   - Don't test "Hi" vs "Hello"
   - Test different approaches

3. Representative samples
   - Random assignment
   - Avoid segment bias

4. Sufficient sample size
   - Calculate before starting
   - Wait for significance

Avoid:
- Peeking and stopping early
- Running too many tests at once
- Testing tiny differences
- Ignoring secondary metrics
- Not accounting for seasonality

Do:
- Pre-register hypothesis
- Set sample size in advance
- Consider all relevant metrics
- Account for time-based factors