What is Minimum Detectable Effect (MDE)?

MDE is the smallest relative percentage improvement in conversion that your experiment is statistically powered to detect. Smaller MDEs (e.g., 2%) require exponentially larger sample sizes than bold changes (e.g., 20%).

Why is 80% statistical power the global standard?

80% power represents the optimal balance between sample collection speed and certainty, ensuring an 80% likelihood of correctly detecting a real conversion difference when one truly exists.

How can early-stage startups with low traffic conduct valid tests?

Early-stage sites should test radical overhauls (targeting MDEs of 30%+), combine micro-conversion metrics, or prioritize qualitative user testing over quantitative split testing.

Why must tests run for at least 7 full calendar days?

User behavior shifts markedly between Monday morning and Saturday evening. Completing full 7-day cycles ensures day-of-week seasonality does not skew experiment findings.

Is any proprietary conversion or traffic data uploaded?

None. All statistical algorithms execute strictly inside your local browser memory with total confidentiality.

A/B Test Sample Size & Test Duration Calculator

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Determine required sample sizes and test duration per variation for conversion rate A/B tests using statistical power and significance thresholds.

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📊 Statistical A/B Sample Size Engine

Hypothesis & Traffic Inputs

Baseline Conversion Rate (%)

Min Detectable Effect (MDE %)

Average Daily Inbound Traffic

Confidence Level

Statistical Power

Required Sample Size per Variant

20,598 Visitors/variant

Estimated Runtime약 21일(~3.0 Weeks)

Total Pool Required41,196명

Statistical Hygiene Checklist

Target Variant CR4.03%

Minimum Test DurationMin 7 Days (Day-of-Week Effect)

No Early Stopping RuleStrictly Required

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A/B Testing Sample Size Mechanics & Statistical Power

In conversion rate optimization (CRO) and digital experimentation, the most frequent failure mode is premature declaration of a winning variant—known formally as the Peeking Problem. If Variant B exhibits a 25% conversion lift after only 3 days of testing with a few hundred visitors, there is an extraordinarily high probability that this apparent lift represents random sampling noise rather than genuine consumer preference.

Halting an A/B test before reaching the mathematically prescribed minimum sample size inflates Type I errors (False Positives) from the intended 5% to over 30% to 50%. This results in engineering and marketing teams permanently deploying changes that either provide zero true value or actively harm long-term revenue.

This statistical calculator models required sample sizes per variant based on baseline conversion rates, minimum detectable effects (MDE), statistical confidence (95%), and power (80%), forecasting the exact calendar duration required based on daily inbound traffic.

A/B Testing Sample Size Mechanics & Hypothesis Testing

Ending an experiment prematurely before reaching statistical power leads to false discoveries (Type I errors). Determining necessary sample size beforehand guarantees decision integrity.

Key Formulas & Practical Calculation ExamplesMathematical Formulation

n = [ 2 × (Z_α/2 + Z_β)^2 × p × (1 - p) ] ÷ (p1 - p2)^2 | Days = (2 × n) ÷ Daily Traffic

Step-by-step practical calculation breakdown demonstrating the formula in action.

A B2B SaaS landing page with a 3.0% baseline conversion rate tests an alternative value proposition targeting a 20% relative lift (target 3.6% CR) with 95% confidence and 80% power.

Step 1Statistical Multipliers: Z_α/2 = 1.96, Z_β = 0.84 ➔ (1.96 + 0.84)^2 = 7.84Multiplier Constant = 7.84

Step 2Sample Size per Variant: [ 2 × 7.84 × 0.03 × 0.97 ] / (0.006)^2 ≈ 0.456 / 0.000036 ≈ 12,670~13,000 visitors per variant (26,000 total)

Step 3Runtime Calculation at 2,000 Daily Visitors: 26,000 total / 2,000/day = 13.0 daysRequires exactly 14 calendar days (2 full weeks)

👉 The experiment must run for a full 14 days without early peeking until both variations accumulate 13,000 unique visitors, guaranteeing decision validity.

Two-Tailed Standard Formula: n = [ 2 × (Z_α/2 + Z_β)^2 × p × (1 - p) ] / (p1 - p2)^2
Confidence Level (95%): Limits false positives (Type I error) to under 5% (Z_α/2 = 1.96).
Statistical Power (80%): 80% probability of successfully detecting a genuine lift if one truly exists (Z_β = 0.84).
Inverse Square Rule: Detecting a tiny 2% lift requires massive traffic cohorts, whereas targeting a bold 20%+ redesign lift resolves with compact sample sizes.
Full Business Cycle Rule: Always run tests for at least 7 to 14 full days to account for day-of-week behavioral variance.

Required Sample Size per Variant Matrix (95% Confidence, 80% Power)

Standard sample sizes required per variation across combinations of baseline rates and relative MDE targets.

Baseline Conversion Rate	MDE +10% Relative Lift	MDE +15% Relative Lift	MDE +20% Relative Lift	MDE +30% Relative Lift
1.0% (E-Commerce Purchase)	152,000 / variant	68,000 / variant	38,500 / variant	17,200 / variant
2.0% (B2B Lead Submission)	76,000 / variant	34,000 / variant	19,200 / variant	8,600 / variant
3.0% (Standard Landing Page)	50,500 / variant	22,500 / variant	12,700 / variant	5,700 / variant
5.0% (Free Trial Signup)	30,000 / variant	13,400 / variant	7,500 / variant	3,400 / variant
10.0% (Key Product Click)	14,200 / variant	6,300 / variant	3,600 / variant	1,600 / variant
20.0% (Top-of-Funnel Micro-Action)	6,300 / variant	2,800 / variant	1,600 / variant	700 / variant

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How to Use

11. Enter Baseline Conversion Rate

Input your current historical conversion rate for the control page (e.g., 3.0% or 5.0%).

22. Set Minimum Detectable Effect (MDE)

Specify the minimum relative lift you expect to reliably detect (e.g., 15% relative improvement).

33. Choose Statistical Confidence & Power

Select confidence level (default 95%, α=0.05) and statistical power (default 80%, β=0.20).

44. Review Sample Size per Variation & Test Duration

Identify required sample size per variation (A vs B) and calculate estimated test runtime given daily traffic.

A/B Experiment Duration Scenarios

Sample size requirements across contrasting traffic volumes.

Baseline CR 3.0%, MDE 20% Lift (Target 3.6%)

Requires ~13,800 visitors per variant (27,600 total). At 2,000 visitors/day, the test concludes cleanly in 14 days.

High-Volume Checkout (10% CR, 10% Lift)

Demands ~11,000 visitors per variant; high-traffic retailers can resolve this test within 3–4 days.

Low-Traffic Early-Stage Startup

Testing subtle 3% button color changes is mathematically infeasible; bold UX overhauls (MDE > 35%) are required.

Holiday Traffic Distortions

Isolating seasonal promotional spikes to prevent temporary purchasing frenzy from corrupting evergreen baseline models.

💡 Pro Tips & Critical Pitfalls

✓ Avoid the Peeking Problem

Do not check conversion results daily and stop the test early when one variant appears ahead; this inflates false-positive errors to over 30%.

✓ Enforce Full Business Cycles (7+ Days)

Even if your sample size is achieved within 3 days, run tests for at least one full week to balance weekday versus weekend buying psychology.

✓ Swing for Bold Swings on Low Traffic

If your monthly traffic is under 20,000 visitors, test radical architectural value proposition redesigns rather than subtle copy tweaks.

⚠️ Common Pitfalls & Mistakes to Avoid

❌ Confusing sample size per variant with total combined traffic

✅ The calculated 'n' represents the required visitors for Variant A and Variant B individually; multiply by 2 for total site traffic.

❌ Testing too many variants simultaneously on moderate traffic

✅ Running an A/B/C/D/E test with 5 variants demands 5x the traffic and extends test runtimes into unmanageable months.

❌ Assuming statistical significance equals business significance

✅ A 0.5% lift that takes 6 months to prove statistically significant may not justify the ongoing maintenance overhead.

❌ Setting statistical power too low (e.g., 50%)

✅ Low statistical power creates Type II errors, discarding genuinely superior design concepts simply because the test was underpowered.

Frequently Asked Questions (FAQ)

Expert answers to the most frequently asked questions and edge cases.

6 Q&As

Known as the 'Peeking Problem', calling early winners based on temporary random fluctuations inflates false positive error rates from 5% to over 30%, destroying experiment validity and leading you to implement false positive changes.

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