A/B Test Sample Size vs Monthly Traffic
Compare A/B test sample requirements with available monthly traffic and see how uplift, confidence, power, and eligibility change duration estimates.
A/B test feasibility depends on both the required sample and the traffic that can enter the experiment. These comparisons show why changing the target uplift, statistical settings, or eligible audience can materially change the expected run time.
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About A/B Test Sample Size vs Monthly Traffic
A/B test feasibility depends on both the required sample and the traffic that can enter the experiment. These comparisons show why changing the target uplift, statistical settings, or eligible audience can materially change the expected run time.
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Comparisons
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Key Factors
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Small uplift versus larger uplift
Compare the effect of choosing a subtle relative conversion improvement versus a larger one at the same baseline rate and traffic level.
| Factor | Option A: Small detectable uplift | Option B: Larger detectable uplift | What It Means |
|---|---|---|---|
| Absolute conversion difference | Smaller difference from the baseline | Larger difference from the baseline | The appropriate threshold depends on what change would be meaningful for the experiment. |
| Visitors needed | Usually higher | Usually lower | Small differences are harder to detect reliably. |
| Estimated test duration | Usually longer | Usually shorter | More required visitors extend the time needed at the same eligible traffic volume. |
| Sensitivity to small improvements | Higher | Lower | A smaller target can identify subtler changes if enough traffic is available. |
| Planning trade-off | More precision, more traffic demand | Faster read on larger effects | Neither target is automatically preferable without considering the decision and traffic constraints. |
A smaller minimum detectable uplift generally demands more traffic and time, while a larger target can make a test feasible sooner but is less sensitive to small improvements.
80% power versus 90% power
Compare common statistical power settings while holding the baseline, uplift, confidence, and traffic inputs constant.
| Factor | Option A: 80% power | Option B: 90% power | What It Means |
|---|---|---|---|
| Typical power z-score | 0.84 | 1.282 | These values represent different planning targets. |
| Required sample | Lower | Higher | Higher power increases the chance of detecting the planned effect when it exists, which requires more observations. |
| Estimated duration | Shorter at the same traffic | Longer at the same traffic | The larger required sample takes longer to collect. |
| Chance of missing the planned effect | Higher than at 90% power | Lower than at 80% power | Power describes detection probability under the assumed effect size and model. |
| Traffic requirement | Less demanding | More demanding | The practical choice depends on the value of additional detection probability and available traffic. |
Moving from 80% to 90% power increases sample requirements and expected duration, but improves the planned ability to detect the selected effect.
All traffic versus filtered eligible traffic
Compare a test that can use all monthly visitors with one restricted to a subset of visitors.
| Factor | Option A: 100% eligible traffic | Option B: Filtered test traffic | What It Means |
|---|---|---|---|
| Monthly visitors entering the test | All stated monthly visitors | Only the eligible share | More eligible traffic reaches the required sample sooner, assuming traffic is appropriate for the experiment. |
| Estimated duration | Shorter | Longer | Duration rises as the eligible monthly visitor count falls. |
| Audience relevance | May be broader | May be more targeted | Restricting eligibility can be necessary when only some visitors can experience the tested change. |
| Traffic calculation | Monthly visitors × 100% | Monthly visitors × test traffic share | The calculator applies the stated eligible share to determine experiment traffic. |
| Interpretation of results | Applies to the broader eligible population | Applies to the filtered population | Results should be interpreted in the context of the audience actually tested. |
Eligibility filtering can be necessary for a relevant experiment, but it reduces the monthly visitor volume available and can extend the estimated test duration.
Key Differences at a Glance
Sample size is driven mainly by baseline conversion rate, target conversion difference, confidence, and power.
Monthly duration additionally depends on the share of visitors eligible for the test.
A relative uplift and an absolute percentage-point change are not interchangeable.
Higher confidence or power generally increases the required visitor count.
Using fewer eligible visitors extends duration even if the statistical sample requirement is unchanged.
How to Decide
Assumptions
- Comparisons assume a binary conversion metric and one control-versus-one-variant experiment.
- The standard sample calculation uses equal traffic allocation.
- All other inputs are assumed constant within each comparison scenario.
- Results are general planning comparisons, not experiment-design or statistical advice.
Related Comparisons
Frequently Asked Questions
Is it better to target a smaller or larger uplift in an A/B test?
It depends on the smallest change that matters for the decision and the traffic available. Smaller targets are more sensitive but generally require more visitors.
Does increasing power make an A/B test more accurate?
It increases the planned probability of detecting the selected effect if it exists, but it also increases the required sample. It does not address tracking or design problems.
Why is eligible traffic more important than total traffic for duration?
Only visitors who can enter the experiment contribute to the test sample, so excluded traffic does not shorten the estimated duration.
Can I compare tests with different baseline conversion rates?
Yes, but use the rate for each specific page, audience, or metric. A different baseline can materially change the sample estimate.
What is the main trade-off in A/B test planning?
The core trade-off is between sensitivity to smaller effects and the traffic and time needed to measure them under the selected statistical settings.
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