A/B Testing Screen Resolution Annual Calculator Examples
Worked examples showing how screen-resolution traffic, detectable effects, and variant count affect annual A/B testing capacity.
These examples show how the same annual site traffic can produce very different testing capacity depending on the resolution segment, available allocation, conversion baseline, and sensitivity required. Results are planning estimates rather than guarantees.
High-traffic desktop segment
A site has 1,000,000 annual visitors. The selected desktop resolution represents 25% of traffic, and 80% of that segment can enter a two-variant test.
Input Summary
Annual visitors
1,000,000
Resolution share
25%
Test allocation
80%
Baseline conversion rate
4%
Minimum detectable effect
20% relative
Variants
2
Calculation Breakdown
- 1Resolution traffic1,000,000 * 25%250,000 visitors
- 2Testable traffic250,000 * 80%200,000 visitors
- 3Total sample required9,408 visitors per variant * 218,816 visitors
- 4Annual capacityfloor(200,000 / 18,816)10 tests
Result Summary
Total sample required
18,816 visitors
A/B Testing Screen Resolution Annual Calculator
The segment supplies 200,000 annual testable visitors, while one test needs about 18,816 visitors.
Low-volume mobile resolution
A site has 300,000 annual visitors. A specific mobile resolution represents 8% of traffic, with 75% available for a two-variant experiment.
Input Summary
Annual visitors
300,000
Resolution share
8%
Test allocation
75%
Baseline conversion rate
3%
Minimum detectable effect
20% relative
Variants
2
Calculation Breakdown
- 1Resolution traffic300,000 * 8%24,000 visitors
- 2Testable traffic24,000 * 75%18,000 visitors
- 3Total sample required16,725 visitors per variant * 233,450 visitors
- 4Annual capacityfloor(18,000 / 33,450)0 tests
Result Summary
Total sample required
33,450 visitors
A/B Testing Screen Resolution Annual Calculator
The segment has about 18,000 testable annual visitors but needs about 33,450 for one test.
Three-variant design on a broad resolution group
A site has 2,000,000 annual visitors. A grouped resolution segment accounts for 40% of traffic, 70% is testable, and the experiment has a control plus two treatments.
Input Summary
Annual visitors
2,000,000
Resolution share
40%
Test allocation
70%
Baseline conversion rate
5%
Minimum detectable effect
25% relative
Variants
3
Calculation Breakdown
- 1Resolution traffic2,000,000 * 40%800,000 visitors
- 2Testable traffic800,000 * 70%560,000 visitors
- 3Sample per variantround((2 * 0.05 * 0.95 * pow(2.80, 2)) / pow(0.0125, 2))4,768 visitors
- 4Total sample and capacity4,768 * 3; floor(560,000 / 14,304)14,304 visitors; 39 tests
Result Summary
Total sample and capacity
14,304 visitors; 39 tests
A/B Testing Screen Resolution Annual Calculator
One three-variant test needs about 14,304 visitors, compared with 560,000 available annually.
How to Read Your Results
Annual testable visitors are the estimated visitors in the chosen segment who can actually enter experiments.
Visitors required per test is the total sample across all variants, not only the treatment group.
Required sample per variant helps check whether every variant can receive enough traffic.
A result of zero annual tests means the available annual segment traffic is lower than the estimated need for one test.
Traffic coverage near or above 100% suggests the test may require most or more than one year's available testable traffic.
Assumptions & Important Notes
- Examples use equal traffic allocation among variants.
- Sample-size figures use approximately 95% two-sided confidence and 80% power unless stated otherwise.
- Each example treats annual visitors as eligible independent observations.
- The conversion baseline is assumed to apply to the selected resolution segment.
Related Examples
Frequently Asked Questions
Can one screen resolution support an A/B test if it shows zero tests annually?
Not at the selected settings according to this estimate. A broader compatible segment, a larger detectable effect, or more traffic may change the estimate.
Why does the low-volume example need more visitors than expected?
Its 3% baseline and 20% relative effect produce a small absolute change of 0.6 percentage points, which requires a larger sample to detect.
Why can the three-variant example still support many tests?
Its grouped segment has much higher testable traffic and a larger absolute effect, which offset the additional variant.
Should I use visitors or sessions in every input?
Use the same unit consistently for annual traffic and the conversion-rate baseline. Avoid mixing visitor-based conversion rates with session counts.
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