A/B Test Users Per Variation vs Total Users
Compare per-variation and total A/B test traffic estimates, plus practical trade-offs in effect size and statistical settings.
A/B test traffic can be viewed per variation or across the entire experiment. This page compares these measures and shows how different planning choices change the sample-size estimate.
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About A/B Test Users Per Variation vs Total Users
A/B test traffic can be viewed per variation or across the entire experiment. This page compares these measures and shows how different planning choices change the sample-size estimate.
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Key Factors
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Users Per Variation vs Total Users
Two ways of reading the same equal-split A/B test requirement.
| Factor | Option A: Users Per Variation | Option B: Total Users Needed | What It Means |
|---|---|---|---|
| Meaning | Users required in control and separately in treatment. | Combined users across both groups. | Both are useful, but they answer different planning questions. |
| Equal 50/50 split | One half of total eligible traffic. | Twice the per-variation figure. | With two equally allocated variations, the figures have a direct two-to-one relationship. |
| Experiment setup | Useful for checking whether each group can reach its target. | Useful for estimating overall traffic demand. | Group-level allocation and overall audience volume are separate considerations. |
| Duration planning | Can be compared with expected daily users in one group. | Can be compared with total eligible users per day. | Use a measure consistent with how traffic is reported. |
Per-variation users are the primary group-size requirement; total users are the combined traffic requirement for the equal-split test.
Small vs Large Minimum Detectable Effect
How the selected meaningful conversion change affects traffic needs.
| Factor | Option A: Small Detectable Effect | Option B: Large Detectable Effect | What It Means |
|---|---|---|---|
| Sample size | Usually much larger. | Usually smaller. | A larger difference is easier to distinguish from random variation. |
| Sensitivity | Can detect subtler changes. | May not identify smaller changes. | The useful choice depends on what change would matter to the decision. |
| Test duration | Often longer at the same traffic level. | Often shorter at the same traffic level. | More required users generally means more time to collect them. |
| Practical relevance | May target changes that are hard to act on. | Can focus on clearly meaningful changes. | A selected effect should be both plausible and useful, not simply convenient. |
Selecting a smaller effect improves sensitivity but can sharply increase the required users per variation.
80% Power vs 90% Power
Comparing two common power settings while keeping other inputs the same.
| Factor | Option A: 80% Power | Option B: 90% Power | What It Means |
|---|---|---|---|
| Probability of detecting the selected effect | Lower than 90% under the model assumptions. | Higher than 80% under the model assumptions. | Higher planned power improves the chance of detecting the chosen effect if it exists. |
| Users per variation | Lower requirement. | Higher requirement. | Greater power requires more observations. |
| Test completion speed | Usually faster with the same traffic. | Usually slower with the same traffic. | The lower sample target can be reached sooner. |
| Planning trade-off | Balances traffic use and detection probability. | Prioritizes greater detection probability. | The suitable setting depends on available traffic and the consequences of missing the selected effect. |
Raising power increases the planned ability to detect the effect but also increases required traffic.
Key Differences at a Glance
Users per variation is a group-level requirement, while total users combines both equal-size groups.
A smaller minimum detectable effect requires more traffic than a larger effect.
Higher confidence increases the statistical threshold and sample-size estimate.
Higher power increases the planned chance of detecting the selected effect and also increases sample size.
Uneven traffic allocation is not represented by the equal-split estimate.
How to Decide
Assumptions
- Comparisons assume a two-variation A/B test with equal allocation.
- The outcome is a binary conversion metric.
- Confidence is two-sided and power is represented through standard normal z-values.
- Results are general planning estimates rather than guarantees of experiment quality.
Related Comparisons
Frequently Asked Questions
Should I plan my A/B test using per-variation users or total users?
Use both. Per-variation users set the group target, while total users helps estimate overall traffic and duration.
Is a smaller minimum detectable effect always better?
Not necessarily. It detects more subtle changes but can require substantially more traffic, so it should reflect a meaningful decision threshold.
Which requires more users: 80% or 90% power?
90% power requires more users when all other inputs are unchanged.
Why is an equal traffic split assumed?
Equal allocation is efficient for a two-group comparison and makes the per-variation estimate directly comparable between control and treatment.
Can I apply this comparison to a multivariate test?
Not directly. More than two variations introduce allocation and multiple-comparison considerations beyond this estimate.
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