Screen Resolution A/B Test: Broad Segment vs Narrow Segment
Compare broad and narrow screen-resolution segments, larger and smaller effects, and different confidence settings when planning A/B test traffic.
A screen-resolution A/B test can be focused or broad, fast or traffic-intensive. These comparisons explain how audience size, detectable effect, and statistical settings change the practical sample-size and duration estimate.
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About Screen Resolution A/B Test: Broad Segment vs Narrow Segment
A screen-resolution A/B test can be focused or broad, fast or traffic-intensive. These comparisons explain how audience size, detectable effect, and statistical settings change the practical sample-size and duration estimate.
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Comparisons
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Key Factors
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Broad screen-resolution segment vs narrow segment
Compare testing a resolution group with a large traffic share against one with limited monthly traffic.
| Factor | Option A: Broad resolution segment | Option B: Narrow resolution segment | What It Means |
|---|---|---|---|
| Eligible monthly traffic | Higher because more visitors match the segment. | Lower because fewer visitors match the segment. | More eligible visitors generally allow the required sample to be collected sooner. |
| Estimated duration | Usually shorter for the same sample requirement. | Usually longer for the same sample requirement. | Duration is total required eligible visitors divided by eligible visitors per month. |
| Audience specificity | May combine visitors with more varied screen experiences. | More focused on one specific experience. | The useful choice depends on whether the change is intended for a broad group or a particular resolution. |
| Result applicability | Potentially relevant to a larger visitor group. | Limited primarily to the targeted segment. | A focused result should not automatically be assumed to represent other screen-resolution groups. |
| Risk of low volume | Lower when the segment is consistently large. | Higher when traffic is sparse or volatile. | Small segments are more sensitive to monthly variation in traffic share. |
Broader segments typically provide faster data collection, while narrow segments can be more specific to a design problem. The suitable scope depends on the audience affected by the change.
Larger detectable effect vs smaller detectable effect
Compare planning for a clearly material conversion change with planning for a subtle change.
| Factor | Option A: Larger minimum detectable effect | Option B: Smaller minimum detectable effect | What It Means |
|---|---|---|---|
| Difference being tested | A larger absolute percentage-point change. | A smaller absolute percentage-point change. | The effect should reflect the smallest change that would be meaningful for the experiment's purpose. |
| Required sample | Generally lower. | Generally higher. | Large differences are easier to distinguish from random variation than small differences. |
| Estimated duration | Usually shorter at the same eligible traffic level. | Usually longer at the same eligible traffic level. | A larger sample requirement increases the time needed to collect eligible visitors. |
| Sensitivity to modest improvements | Less sensitive to small changes. | More sensitive to small changes. | A smaller effect target is designed to detect a more subtle conversion difference. |
| Planning practicality for low-volume segments | More likely to be feasible. | May become impractical if traffic is limited. | Narrow screen-resolution segments may not produce enough traffic for a small-effect test in a reasonable period. |
Choosing a smaller effect improves sensitivity to subtle changes but can sharply increase the visitors and time required. The effect size is a planning choice, not a target to optimize mechanically.
95% confidence vs 99% confidence
Compare two evidence thresholds using the same baseline rate, effect size, power, and resolution traffic.
| Factor | Option A: 95% confidence | Option B: 99% confidence | What It Means |
|---|---|---|---|
| Confidence z-score | 1.96 | 2.576 | The z-score is the statistical input that represents the selected confidence setting. |
| Required sample | Lower under otherwise identical inputs. | Higher under otherwise identical inputs. | The stricter 99% setting increases the calculation's evidence threshold. |
| Estimated duration | Usually shorter. | Usually longer. | More required visitors generally means more time at the same monthly eligible traffic. |
| False-positive threshold | Less strict than 99% in this planning comparison. | Stricter than 95% in this planning comparison. | A higher confidence setting is designed to require stronger evidence before identifying a difference. |
| Traffic feasibility | More feasible for lower-volume resolution segments. | May be difficult for low-volume segments. | The appropriate setting depends on the experiment context and the available traffic. |
Moving from 95% to 99% confidence increases the sample needed and can lengthen a resolution-specific test. Neither setting is automatically best for every experiment.
Key Differences at a Glance
Eligible traffic is determined by the selected resolution's share of total website visitors.
A broader segment can shorten duration but may answer a less specific experience question.
A smaller minimum detectable effect requires a larger sample than a larger effect.
Higher confidence increases the sample requirement when other inputs remain unchanged.
Higher power also increases the estimated visitors needed per variation.
Results for one screen-resolution segment may not generalize to every device or viewport group.
How to Decide
Assumptions
- All comparisons use an evenly split two-variation A/B test.
- Traffic, segment share, and baseline conversion rate are assumed broadly stable during the test.
- The calculator uses a two-sided comparison of conversion proportions.
- The comparisons do not include adjustments for multiple tests, exclusions, or other experimental design choices.
Related Comparisons
Frequently Asked Questions
Is a broader screen-resolution segment always better for an A/B test?
Not always. It usually provides more traffic, but a narrower segment may be more relevant when the design change addresses a specific screen experience.
Why does a smaller detectable effect increase test duration?
Detecting a smaller difference requires more observations, so the total eligible sample grows at the same traffic level.
Does 99% confidence always produce better results than 95% confidence?
It uses a stricter threshold and generally requires more traffic. The practical trade-off depends on the experiment context and available volume.
Can I compare different screen resolutions with this calculator?
You can calculate each resolution segment separately using its own traffic share and baseline conversion rate, then compare the resulting planning estimates.
What should I compare besides sample size?
Consider segment relevance, traffic stability, measurement quality, the effect size that matters, and whether the result applies to the intended user experience.
Ready to calculate your result?
Try the calculator and compare options with your own inputs.