A/B Test Sample Size: Small vs Large Minimum Detectable Effect
Compare small and large A/B test minimum detectable effects and see how target uplift, confidence, power, and traffic allocation affect sample size.
A/B test planning involves trade-offs. A smaller uplift can be more sensitive to incremental gains, but it takes more traffic; a larger uplift needs less traffic but may miss meaningful smaller improvements. These comparisons outline the practical differences.
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About A/B Test Sample Size: Small vs Large Minimum Detectable Effect
A/B test planning involves trade-offs. A smaller uplift can be more sensitive to incremental gains, but it takes more traffic; a larger uplift needs less traffic but may miss meaningful smaller improvements. These comparisons outline the practical differences.
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Key Factors
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Small MDE vs Large MDE
Compare a test designed to detect a 5% relative uplift with one designed to detect a 20% relative uplift from the same 10% baseline conversion rate.
| Factor | Option A: 5% Relative Uplift | Option B: 20% Relative Uplift | What It Means |
|---|---|---|---|
| Target variant conversion rate | 10.5% | 12% | The smaller MDE targets a 0.5-percentage-point change; the larger MDE targets a 2-percentage-point change. |
| Visitors per variation at 95% confidence and 80% power | Approximately 60,374 | Approximately 3,837 | Larger conversion-rate differences require far fewer observations to detect. |
| Sensitivity to smaller improvements | Can detect a smaller planned improvement | May not detect improvements below the target | The test is powered around its chosen effect size, not every possible change. |
| Traffic requirement | High | Lower | A small MDE may not be feasible for a low-traffic experiment. |
| Decision usefulness | Useful when small gains would justify implementation | Useful when only substantial gains matter | The appropriate MDE is tied to the practical importance of the decision, not statistical preference alone. |
Choose an MDE based on the smallest change that would be meaningful and feasible to test with available eligible traffic.
80% Power vs 90% Power
Compare two common power settings for a 10% baseline conversion rate and a 20% relative uplift at 95% confidence.
| Factor | Option A: 80% Power | Option B: 90% Power | What It Means |
|---|---|---|---|
| Power Z-score | 0.84 | 1.282 | The higher power setting uses a larger Z-score in the sample-size formula. |
| Visitors per variation | Approximately 3,837 | Approximately 5,133 | Higher power increases the number of visitors required per group. |
| Chance of detecting the target effect if present | Lower modeled probability | Higher modeled probability | Power describes the model-based ability to identify the planned effect when it truly exists. |
| Test duration at fixed traffic | Shorter | Longer | More required visitors generally means more calendar time at the same eligible traffic level. |
| Planning trade-off | Lower traffic requirement | Greater sensitivity to the target effect | The choice depends on available traffic and the consequences of failing to detect a meaningful effect. |
Increasing power can improve the planned ability to detect the target uplift, but it requires more observations and usually extends test duration.
50/50 Split vs Uneven Traffic Split
Compare an equal allocation with an uneven allocation in a two-variation conversion test.
| Factor | Option A: 50/50 Traffic Split | Option B: Uneven Traffic Split | What It Means |
|---|---|---|---|
| Statistical efficiency for a fixed total sample | Highest for two equal-sized groups | Lower when groups are materially unequal | For the same total audience, balanced groups generally provide more efficient comparison information. |
| Required total traffic | Lower under the calculator assumptions | May be higher | Unequal allocation can require additional total visitors to achieve comparable sensitivity. |
| Control exposure | Half of eligible traffic remains on control | Can preserve more control traffic | An uneven allocation may be selected for operational reasons, but it changes the statistical plan. |
| Match to this calculator | Direct match | Not directly modeled | The calculator assumes visitors are split evenly between control and variant. |
| Interpretation simplicity | Simpler | Requires allocation-aware planning | Balanced allocation makes group-size targets and duration estimates easier to apply. |
The calculator's result is designed for an even split. If allocation is deliberately uneven, use a method that explicitly incorporates the planned ratio.
Key Differences at a Glance
The minimum detectable effect determines the smallest planned uplift, while power measures sensitivity to that target effect.
Smaller absolute conversion-rate differences require substantially more visitors.
Higher confidence and higher power both increase the required sample size.
The calculator assumes equal allocation, which is generally more statistically efficient than a strongly uneven split.
Daily eligible traffic affects estimated duration but does not change the underlying visitor requirement.
How to Decide
Assumptions
- All comparisons refer to two independent variations and a binary conversion metric.
- Illustrative visitor counts use the standard two-proportion approximation used by the calculator.
- Sample-size values assume a stable baseline rate and broadly representative traffic.
- The 50/50 allocation comparison reflects the calculator's equal-split design.
- These comparisons are educational planning information, not a guarantee of test results.
Related Comparisons
Frequently Asked Questions
Is a smaller minimum detectable effect always better?
Not necessarily. It provides sensitivity to smaller changes but can require far more traffic. The useful choice depends on which changes would matter in practice.
Should I always choose 90% power?
Not always. Higher power increases sample size. The appropriate setting depends on traffic availability and the importance of detecting the planned effect.
Why does an even traffic split help A/B test efficiency?
With a fixed total audience, similarly sized control and variant groups generally provide a more efficient comparison than materially unequal groups.
Can I compare a 5% uplift test with a 20% uplift test directly?
Yes for planning purposes, but they answer different questions because each test is designed to detect a different minimum effect.
Does more daily traffic reduce the required sample size?
No. It mainly reduces the estimated time needed to reach the same required sample size.
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