Sample Size Guide
Reference for calculating sample sizes and test duration.
Contents
- Sample Size Fundamentals (required inputs, what these mean)
- Sample Size Quick Reference Tables
- Duration Calculator (formula, examples, minimum duration rules, maximum duration guidelines)
- Online Calculators
- Adjusting for Multiple Variants
- Common Sample Size Mistakes
- When Sample Size Requirements Are Too High
- Sequential Testing
- Quick Decision Framework
Sample Size Fundamentals
Required Inputs
- Baseline conversion rate: Your current rate
- Minimum detectable effect (MDE): Smallest change worth detecting
- Statistical significance level: Usually α = 0.05 (5%)
- 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?)
Significance level (α = 0.05): For the planned test and its assumptions, limits the long-run probability of rejecting a true null hypothesis to 5%. It is not the probability that an observed difference is “due to chance.” Repeated unadjusted looks or multiple comparisons do not retain that error rate. See the ASA's interpretation guidance.
Statistical power (80%): Means if there's a real effect of size MDE, you have 80% chance of detecting it.
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 |
Duration Calculator
Formula
B0
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
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
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.
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
When Sample Size Requirements Are Too High
Options when you can't get enough traffic:
- Increase MDE: Accept only detecting larger effects (20%+ lift)
- Lower confidence: Use 90% instead of 95% (risky, document it)
- Reduce variants: Test only the most promising variant
- Combine traffic: Test across multiple similar pages
- Test upstream: Test earlier in funnel where traffic is higher
- Don't test: Make decision based on qualitative data instead
- Longer test: Accept longer duration (weeks/months)
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
Quick Decision Framework
Can I run this test?
B1