A/B Testing Screen Resolution Monthly Calculator FAQ
Answers to common questions about screen-resolution A/B test traffic, sample size, effect size, confidence, power, and duration estimates.
Use these answers to understand the inputs and planning estimates for an A/B test limited to a particular screen resolution. Results are educational estimates and should be interpreted alongside experiment design and measurement quality.
General questions
Core concepts behind testing a resolution-specific visitor segment.
What is a screen resolution A/B test?
It compares two versions of an experience among visitors classified in a selected screen resolution segment.
Why run an A/B test by screen resolution?
A layout or interaction may perform differently at different viewport or resolution sizes, so a focused segment can help evaluate that experience.
What traffic is eligible for the test?
Eligible traffic is total monthly visitors multiplied by the selected resolution's share of traffic.
Does this calculator test more than two variations?
No. It estimates an evenly split control-versus-variant A/B test. Additional variations generally need a separate design and may require more total traffic.
Inputs and calculation
How conversion rates, effect size, and statistical settings affect the estimate.
What baseline conversion rate should I enter?
Use the current conversion rate for visitors in the selected screen resolution segment when a reliable segment-specific estimate is available.
What is a minimum detectable effect?
It is the smallest absolute conversion-rate difference that the test is intended to detect. It is entered in percentage points.
How do I enter a change from 5% to 6%?
Enter 5% as the baseline conversion rate and 1 percentage point as the minimum detectable effect.
How is the variant conversion rate calculated?
The calculator adds the absolute minimum detectable effect to the baseline conversion rate.
Why is traffic split between two groups?
The formula estimates a control and variant group, each receiving half of eligible visitors under the stated assumption.
Confidence, power, and duration
What the statistical planning settings mean for traffic requirements.
What confidence level does the calculator support?
It supports the supplied planning choices of 90%, 95%, and 99% confidence through their corresponding z-scores.
What does statistical power mean?
Power is the planned probability of detecting the selected effect when it is truly present. Higher power raises the required sample.
Why does higher confidence require more visitors?
A higher confidence setting uses a larger z-score, which increases the evidence threshold in the sample-size calculation.
How is test duration estimated?
Total required eligible visitors are divided by estimated eligible visitors per month.
Is a duration of 1.5 months an exact calendar prediction?
No. It is a traffic-based estimate. Actual calendar time depends on day-to-day and week-to-week traffic patterns.
Accuracy and use
Factors that can make observed outcomes differ from the planning estimate.
Are the results guaranteed?
No. They are planning estimates based on entered values and statistical assumptions, not guaranteed experiment outcomes.
Does the calculation account for seasonal traffic changes?
No. It assumes broadly stable monthly traffic and screen-resolution share.
Does it account for multiple metrics or multiple tests?
No. Those choices can change how an experiment should be planned and interpreted.
Should I end a test early when one version appears ahead?
Early results can fluctuate. A preplanned sample and evaluation approach helps avoid interpreting short-term variation as a stable difference.
How is A/B test duration for a screen resolution calculated?
The calculator divides the total eligible sample required for control and variant by the estimated monthly visitors in the chosen resolution segment.
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