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A/B Testing Screen Resolution Calculator Formula

Learn how screen-resolution segment traffic, visitor allocation, test duration, and expected conversions are estimated for an A/B test.

This calculator estimates whether a selected screen-resolution segment can supply enough visitors for an A/B test. It converts total daily traffic into segment traffic, divides that traffic among variations, and estimates the calendar days needed to reach a chosen visitor target per variation.

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Estimated Test Duration

Estimated test days = ceil((Target visitors per variation × Number of variations) ÷ (Daily visitors × Resolution traffic share ÷ 100))

Where:

First estimate how many visitors use the selected resolution each day. Then calculate how many segment visitors all variations need in total and divide that requirement by daily segment traffic. The result is rounded up to a whole day.

Variables Explained

VariableWhat It MeansUnit
dailyVisitors - Total daily visitorsAverage daily eligible visitors to the site or experiment.visitors/day
resolutionTrafficShare - Resolution traffic sharePercentage of eligible visitors using the selected screen-resolution or viewport segment.percent
variants - Number of variationsTotal versions sharing test traffic, including the control.number
requiredVisitorsPerVariant - Target visitors per variationThe chosen minimum number of segment visitors each variation should receive.visitors
expectedConversionRate - Expected conversion rateExpected percentage of segment visitors who complete the measured conversion.percent

Step-by-Step Calculation

1

Estimate daily segment traffic

Multiply total eligible daily traffic by the selected resolution segment's percentage share.

dailyResolutionVisitors = dailyVisitors * (resolutionTrafficShare / 100)

2

Estimate traffic per variation

With an equal traffic split, divide daily segment visitors by the number of variations.

dailyVisitorsPerVariant = dailyResolutionVisitors / variants

3

Calculate total segment visitors needed

Every variation needs its own target number of visitors, so multiply the target by the variation count.

totalRequiredResolutionVisitors = requiredVisitorsPerVariant * variants

4

Estimate the duration

Divide all required segment visitors by daily segment traffic and round upward to a whole calendar day.

estimatedTestDays = ceil(totalRequiredResolutionVisitors / dailyResolutionVisitors)

5

Estimate conversions per variation

Apply the expected conversion rate to the visitor target for one variation.

expectedConversionsPerVariant = requiredVisitorsPerVariant * (expectedConversionRate / 100)

Example: 1920 × 1080 desktop layout test

Screen resolution1920 × 1080
Total daily visitors10,000 visitors/day
Resolution traffic share18%
Number of variations2
Target visitors per variation1,000 visitors
Expected conversion rate5%
1

Daily resolution visitors

10,000 × (18 ÷ 100)

1,800 visitors/day

2

Daily visitors per variation

1,800 ÷ 2

900 visitors/day

3

Total required segment visitors

1,000 × 2

2,000 visitors

4

Estimated test duration

ceil(2,000 ÷ 1,800)

2 days

5

Expected conversions per variation

1,000 × (5 ÷ 100)

50 conversions

Final Result

The 1920 × 1080 segment is estimated to need about 2 calendar days to deliver 1,000 visitors to each of two variations, with about 50 expected conversions per variation.

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Assumptions

  • Traffic from the selected screen-resolution segment remains broadly stable throughout the test.
  • Eligible segment traffic is split equally among all listed variations.
  • The visitor target per variation has been selected separately using an appropriate experiment-planning method.
  • Each eligible visitor is treated as one observation, without adjusting for repeat visits, exclusions, or identity stitching.
  • The reported screen-resolution segment is measured consistently by the analytics or experimentation tool.

Limitations

  • !This is a traffic-planning estimate, not a statistical significance calculation.
  • !A visitor target alone may be insufficient when the conversion rate is low or the expected improvement is small.
  • !Traffic can vary by weekday, campaign activity, seasonality, geography, and device mix.
  • !Reported screen resolution may differ from browser viewport dimensions, CSS pixels, device pixel ratio, or the layout breakpoint that matters.
  • !Rounding duration to full days does not account for partial-day launches or uneven traffic by hour.

Common Mistakes to Avoid

1

Entering the segment share as 0.18 when the field expects 18%.

2

Excluding the control when counting the total number of variations.

3

Using total site visitors instead of eligible visitors who can actually enter the experiment.

4

Treating reported screen resolution as identical to viewport width or responsive breakpoint.

5

Choosing a visitor target without considering baseline conversion rate and the effect size being evaluated.

6

Ending the test automatically when the estimated number of days has passed.

Related Formulas

Frequently Asked Questions

What is the formula for A/B test duration by screen resolution?

The estimate is ceil((target visitors per variation × variations) ÷ daily screen-resolution visitors). Daily screen-resolution visitors equal total daily eligible visitors multiplied by the segment share.

How are daily visitors for a screen resolution calculated?

Multiply daily eligible visitors by the resolution segment's percentage share. For example, 12,000 daily visitors and a 15% share produce 1,800 segment visitors per day.

Why does adding another variation increase test duration?

Each variation needs its own visitor target. Adding a variation increases the total segment traffic required while the available segment traffic stays the same.

How are expected conversions per variation calculated?

Multiply the target visitors per variation by the expected conversion rate expressed as a decimal. A target of 2,000 visitors at 3% produces 60 expected conversions.

Does this formula calculate statistical significance?

No. It estimates traffic allocation and timing. Statistical planning also requires assumptions about baseline performance, detectable effect size, error thresholds, and test design.

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