A/B Testing Screen Resolution Per-User Calculator Examples
Worked examples showing how baseline conversion rate, detectable change, confidence, and power affect A/B test traffic needs.
These examples illustrate how the calculator estimates users needed in each group for a two-variation conversion test. They use an equal traffic split and an absolute change in conversion rate.
Signup test with a moderate baseline
A website has a 10% signup conversion rate and is testing a revised registration flow.
Input Summary
Baseline conversion rate
10%
Minimum detectable effect
1 percentage point
Confidence and power
95% confidence, 80% power
Calculation Breakdown
- 1Target treatment rate10% + 1 percentage point11%
- 2Required users per variationTwo-proportion sample-size approximation14,739 users
- 3Total traffic14,739 × 229,478 users
Result Summary
Total traffic
29,478 users
A/B Testing Screen Resolution Per-User Calculator
The test requires about 14,739 users in each variation.
Low-conversion purchase test
The current purchase rate is 2%, and the team wants to detect a 0.5 percentage-point change.
Input Summary
Baseline conversion rate
2%
Minimum detectable effect
0.5 percentage point
Confidence and power
95% confidence, 80% power
Calculation Breakdown
- 1Target treatment rate2% + 0.5 percentage point2.5%
- 2Required users per variationTwo-proportion sample-size approximation12,300 users
- 3Total traffic12,300 × 224,600 users
Result Summary
Total traffic
24,600 users
A/B Testing Screen Resolution Per-User Calculator
The test needs approximately 12,300 users in each variation.
High-volume lead form with a small target change
The landing page converts 25% of visitors, and a 0.5 percentage-point increase would matter.
Input Summary
Baseline conversion rate
25%
Minimum detectable effect
0.5 percentage point
Confidence and power
95% confidence, 80% power
Calculation Breakdown
- 1Target treatment rate25% + 0.5 percentage point25.5%
- 2Required users per variationTwo-proportion sample-size approximation117,373 users
- 3Total traffic117,373 × 2234,746 users
Result Summary
Total traffic
234,746 users
A/B Testing Screen Resolution Per-User Calculator
The test requires about 117,373 users per variation.
Stricter power for a trial-start test
The free-trial start rate is 8%, with a target change of 2 percentage points and 90% power.
Input Summary
Baseline conversion rate
8%
Minimum detectable effect
2 percentage points
Confidence and power
95% confidence, 90% power
Calculation Breakdown
- 1Target treatment rate8% + 2 percentage points10%
- 2Required users per variationTwo-proportion sample-size approximation using z = 1.282 for power4,292 users
- 3Total traffic4,292 × 28,584 users
Result Summary
Total traffic
8,584 users
A/B Testing Screen Resolution Per-User Calculator
The test needs around 4,292 users in each variation.
How to Read Your Results
Users per variation is the target number of eligible users for control and treatment separately.
Total users is the combined estimate for a two-group test with a 50/50 traffic split.
The minimum detectable effect is an absolute percentage-point change, not a relative lift.
Use eligible users who can genuinely reach the conversion opportunity.
Treat the result as a planning estimate rather than a guarantee of a valid outcome.
Assumptions & Important Notes
- All examples use one binary conversion metric and two independent variations.
- Traffic is split equally between control and treatment.
- Confidence is two-sided, and the listed power represents the desired detection probability if the effect is present.
- Example values are rounded for readability.
Related Examples
Frequently Asked Questions
Can I estimate test duration from these examples?
Yes, divide total users needed by the average number of eligible users available per day, then allow for normal traffic variation.
Why does the high-conversion example need more traffic?
It targets only a 0.5 percentage-point change. The smaller absolute difference is the main reason the estimate is large.
Should I choose the largest possible minimum detectable effect?
Choose a change that would be meaningful for the decision. A larger selected effect reduces traffic needs but may miss smaller meaningful changes.
Do the examples apply to mobile and desktop separately?
They apply only if each segment is tested and analyzed as its own eligible population. Splitting traffic into segments can increase the time needed.
Ready to calculate your own result?
Use the live calculator with your own inputs, timing, and preferences.