claude-skills-reference/marketing-skill/ab-test-setup/references/sample-size-guide.md

Sample Size Guide

Reference for calculating sample sizes and test duration.

Sample Size Fundamentals

Required Inputs

1. Baseline conversion rate: Your current rate
2. Minimum detectable effect (MDE): Smallest change worth detecting
3. Statistical significance level: Usually 95% (α = 0.05)
4. Statistical power: Usually 80% (β = 0.20)

What These Mean

Baseline conversion rate: If your page converts at 5%, that's your baseline.

MDE (Minimum Detectable Effect): The smallest improvement you care about detecting. Set this based on:

• Business impact (is a 5% lift meaningful?)
• Implementation cost (worth the effort?)
• Realistic expectations (what have past tests shown?)

Statistical significance (95%): Means there's less than 5% chance the observed difference is due to random chance.

Statistical power (80%): Means if there's a real effect of size MDE, you have 80% chance of detecting it.

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Sample Size Quick Reference Tables

Conversion Rate: 1%

Lift to Detect	Sample per Variant	Total Sample
5% (1% → 1.05%)	1,500,000	3,000,000
10% (1% → 1.1%)	380,000	760,000
20% (1% → 1.2%)	97,000	194,000
50% (1% → 1.5%)	16,000	32,000
100% (1% → 2%)	4,200	8,400

Conversion Rate: 3%

Lift to Detect	Sample per Variant	Total Sample
5% (3% → 3.15%)	480,000	960,000
10% (3% → 3.3%)	120,000	240,000
20% (3% → 3.6%)	31,000	62,000
50% (3% → 4.5%)	5,200	10,400
100% (3% → 6%)	1,400	2,800

Conversion Rate: 5%

Lift to Detect	Sample per Variant	Total Sample
5% (5% → 5.25%)	280,000	560,000
10% (5% → 5.5%)	72,000	144,000
20% (5% → 6%)	18,000	36,000
50% (5% → 7.5%)	3,100	6,200
100% (5% → 10%)	810	1,620

Conversion Rate: 10%

Lift to Detect	Sample per Variant	Total Sample
5% (10% → 10.5%)	130,000	260,000
10% (10% → 11%)	34,000	68,000
20% (10% → 12%)	8,700	17,400
50% (10% → 15%)	1,500	3,000
100% (10% → 20%)	400	800

Conversion Rate: 20%

Lift to Detect	Sample per Variant	Total Sample
5% (20% → 21%)	60,000	120,000
10% (20% → 22%)	16,000	32,000
20% (20% → 24%)	4,000	8,000
50% (20% → 30%)	700	1,400
100% (20% → 40%)	200	400

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Duration Calculator

Formula

Duration (days) = (Sample per variant × Number of variants) / (Daily traffic × % exposed)

Examples

Scenario 1: High-traffic page

• Need: 10,000 per variant (2 variants = 20,000 total)
• Daily traffic: 5,000 visitors
• 100% exposed to test
• Duration: 20,000 / 5,000 = 4 days

Scenario 2: Medium-traffic page

• Need: 30,000 per variant (60,000 total)
• Daily traffic: 2,000 visitors
• 100% exposed
• Duration: 60,000 / 2,000 = 30 days

Scenario 3: Low-traffic with partial exposure

• Need: 15,000 per variant (30,000 total)
• Daily traffic: 500 visitors
• 50% exposed to test
• Effective daily: 250
• Duration: 30,000 / 250 = 120 days (too long!)

Minimum Duration Rules

Even with sufficient sample size, run tests for at least:

• 1 full week: To capture day-of-week variation
• 2 business cycles: If B2B (weekday vs. weekend patterns)
• Through paydays: If e-commerce (beginning/end of month)

Maximum Duration Guidelines

Avoid running tests longer than 4-8 weeks:

• Novelty effects wear off
• External factors intervene
• Opportunity cost of other tests

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Online Calculators

Recommended Tools

Evan Miller's Calculator https://www.evanmiller.org/ab-testing/sample-size.html

• Simple interface
• Bookmark-worthy

Optimizely's Calculator https://www.optimizely.com/sample-size-calculator/

• Business-friendly language
• Duration estimates

AB Test Guide Calculator https://www.abtestguide.com/calc/

• Includes Bayesian option
• Multiple test types

VWO Duration Calculator https://vwo.com/tools/ab-test-duration-calculator/

• Duration-focused
• Good for planning

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Adjusting for Multiple Variants

With more than 2 variants (A/B/n tests), you need more sample:

Variants	Multiplier
2 (A/B)	1x
3 (A/B/C)	~1.5x
4 (A/B/C/D)	~2x
5+	Consider reducing variants

Why? More comparisons increase chance of false positives. You're comparing:

• A vs B
• A vs C
• B vs C (sometimes)

Apply Bonferroni correction or use tools that handle this automatically.

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Common Sample Size Mistakes

1. Underpowered tests

Problem: Not enough sample to detect realistic effects Fix: Be realistic about MDE, get more traffic, or don't test

2. Overpowered tests

Problem: Waiting for sample size when you already have significance Fix: This is actually fine—you committed to sample size, honor it

3. Wrong baseline rate

Problem: Using wrong conversion rate for calculation Fix: Use the specific metric and page, not site-wide averages

4. Ignoring segments

Problem: Calculating for full traffic, then analyzing segments Fix: If you plan segment analysis, calculate sample for smallest segment

5. Testing too many things

Problem: Dividing traffic too many ways Fix: Prioritize ruthlessly, run fewer concurrent tests

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When Sample Size Requirements Are Too High

Options when you can't get enough traffic:

1. Increase MDE: Accept only detecting larger effects (20%+ lift)
2. Lower confidence: Use 90% instead of 95% (risky, document it)
3. Reduce variants: Test only the most promising variant
4. Combine traffic: Test across multiple similar pages
5. Test upstream: Test earlier in funnel where traffic is higher
6. Don't test: Make decision based on qualitative data instead
7. Longer test: Accept longer duration (weeks/months)

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Sequential Testing

If you must check results before reaching sample size:

What is it?

Statistical method that adjusts for multiple looks at data.

When to use

• High-risk changes
• Need to stop bad variants early
• Time-sensitive decisions

Tools that support it

• Optimizely (Stats Accelerator)
• VWO (SmartStats)
• PostHog (Bayesian approach)

Tradeoff

• More flexibility to stop early
• Slightly larger sample size requirement
• More complex analysis

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Quick Decision Framework

Can I run this test?

Daily traffic to page: _____
Baseline conversion rate: _____
MDE I care about: _____

Sample needed per variant: _____ (from tables above)
Days to run: Sample / Daily traffic = _____

If days > 60: Consider alternatives
If days > 30: Acceptable for high-impact tests
If days < 14: Likely feasible
If days < 7: Easy to run, consider running longer anyway