A/B Test Uptime: Equal Split vs Limited Rollout
Compare equal-split and limited-rollout A/B test uptime calculations, including their effects on average availability and user downtime exposure.
Traffic allocation determines how much each variant influences average uptime and how broadly its downtime exposure is distributed. These comparisons explain the trade-offs between equal allocation, limited rollout, shorter tests, and longer tests using availability estimates.
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About A/B Test Uptime: Equal Split vs Limited Rollout
Traffic allocation determines how much each variant influences average uptime and how broadly its downtime exposure is distributed. These comparisons explain the trade-offs between equal allocation, limited rollout, shorter tests, and longer tests using availability estimates.
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Key Factors
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Equal Traffic Split vs Limited Rollout
Compare a 50/50 experiment with a 10/90 rollout when one variant has lower uptime.
| Factor | Option A: 50/50 Equal Split | Option B: 10% Limited Rollout | What It Means |
|---|---|---|---|
| Influence of each variant on weighted uptime | Each variant has equal influence. | The 10% variant has limited influence. | Equal allocation is neutral between variants, while limited allocation makes the established variant dominate the population average. |
| Exposure to a lower-uptime variant | Half of test users are assigned to it. | One-tenth of test users are assigned to it. | A smaller allocation reduces the number of users expected to encounter the lower-uptime variant. |
| Average per-user downtime | More strongly affected by the lower-uptime variant. | Less strongly affected when the lower-uptime variant receives 10%. | Weighted downtime follows the same traffic shares as weighted uptime. |
| Users assigned to each variant | Balanced population sizes. | Uneven population sizes. | The calculator estimates exposure, not experiment sensitivity or statistical requirements. |
| Total lost user-hours | Usually higher if the lower-uptime variant receives 50% of traffic. | Usually lower if the lower-uptime variant receives 10% of traffic. | With the same user count and duration, less traffic sent to the lower-uptime variant reduces estimated aggregate downtime. |
An equal split gives both variants equal weight in availability results, while a limited rollout reduces the impact of a lower-uptime variant on the broader user population.
Short Test vs Long Test at the Same Uptime
Compare the effect of duration when variant uptime and traffic allocation do not change.
| Factor | Option A: Short Test | Option B: Long Test | What It Means |
|---|---|---|---|
| Weighted uptime percentage | Unchanged for the same uptimes and traffic shares. | Unchanged for the same uptimes and traffic shares. | Weighted uptime is a percentage and does not depend on the duration entered. |
| Downtime per user | Lower because the period has fewer hours. | Higher because the period has more hours. | Expected downtime is the unavailable share multiplied by total test hours. |
| Total lost user-hours | Lower with the same total user count. | Higher with the same total user count. | Aggregate lost user-hours rise with longer duration when other inputs remain constant. |
| Time covered by the estimate | Models a shorter exposure window. | Models a longer exposure window. | The useful duration depends on the period being evaluated. |
| Uptime difference in percentage points | The same difference between variants. | The same difference between variants. | Subtracting one uptime percentage from the other does not use test duration. |
Changing duration does not change the weighted uptime percentage, but it directly changes expected downtime hours and total lost user-hours.
Higher Uptime Variant vs Higher Traffic Variant
Compare two ways a variant can shape the average user availability result.
| Factor | Option A: Higher Uptime Variant | Option B: Higher Traffic Variant | What It Means |
|---|---|---|---|
| Effect on weighted uptime | Improves the average when it receives traffic. | Increases the influence of its own uptime level. | The weighted result depends on both the uptime gap and the traffic allocation. |
| Effect when uptime values are equal | No additional effect because both variants are equally available. | No effect on average uptime. | Traffic allocation does not change the weighted average when both uptime inputs match. |
| Effect when one variant has lower uptime | Higher uptime offsets lower availability in the other variant. | More traffic to the lower-uptime variant reduces the average. | A better uptime value raises the weighted result, while more allocation to a lower value pulls it down. |
| Effect on per-user downtime | Reduces downtime for users assigned to that variant. | Spreads that variant's downtime exposure to more users. | Whether higher traffic improves or worsens exposure depends on that variant's uptime. |
| Effect on total lost user-hours | Lower uptime loss can reduce aggregate exposure. | Higher allocation can increase or reduce exposure depending on uptime. | Total exposure combines user count, duration, traffic allocation, and both downtime rates. |
Uptime quality and traffic allocation work together. A variant with strong uptime improves the average most when it receives more traffic, while a lower-uptime variant creates more exposure as its allocation rises.
Key Differences at a Glance
Traffic share determines how much each variant contributes to weighted uptime and average downtime.
Test duration changes downtime hours and total lost user-hours, but not the weighted uptime percentage.
Total user count changes aggregate lost user-hours, but not per-user uptime or downtime.
A percentage-point uptime difference is independent of traffic allocation, while the average user result is not.
Limited rollouts can reduce population-level exposure to a lower-uptime variant.
How to Decide
Assumptions
- Each comparison assumes users remain assigned according to the stated traffic allocation.
- The variants' uptime levels are assumed to remain stable over the modeled duration.
- Users are treated as having equal potential exposure to the service during the test.
- The comparisons estimate availability time rather than incident severity or business impact.
Related Comparisons
Frequently Asked Questions
Is a 50/50 traffic split always worse for uptime exposure?
No. It depends on which variant has lower uptime. A 50/50 split gives each variant equal influence on the average result.
Does a longer A/B test lower uptime?
Not in this calculation. The uptime percentage stays the same when variant uptime and traffic shares stay the same, but downtime hours increase.
Does sending more traffic to the higher-uptime variant improve average uptime?
Yes. When one variant has higher uptime, assigning it a larger traffic share raises the traffic-weighted average.
Which result should I compare first: uptime or lost user-hours?
Weighted uptime shows average availability, while lost user-hours show aggregate exposure. Reviewing both provides a fuller estimate.
Can I use the comparison to measure experiment quality?
It compares estimated availability exposure only. It does not evaluate experimental validity, product outcomes, or the quality of the tested experience.
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