A/B Testing Core Web Vital Per-User Calculator FAQ
Answers to common questions about comparing LCP, INP, and CLS between test variants and estimating conversion impact.
Use these answers to understand the calculator inputs, calculation method, and practical limits of interpreting Core Web Vital A/B test results.
Getting started
Basic questions about what the calculator compares.
What does the A/B Testing Core Web Vital Per-User Calculator do?
It compares a control and variant metric, then estimates aggregate metric reduction, observed conversion lift, and conversion-based value for variant users.
Which Core Web Vitals can I enter?
The calculator supports Largest Contentful Paint, Interaction to Next Paint, and Cumulative Layout Shift.
What does per-user mean in this calculator?
It refers to the average metric difference between the control and variant for an individual exposed user.
Who should use this calculator?
It is intended for teams reviewing web performance experiments alongside conversion data.
Inputs and measurement
Questions about entering comparable experiment data.
Should control and variant data come from the same source?
Yes. Use the same metric definition, collection method, device scope, and comparable measurement period where possible.
How should I enter CLS?
Enter a score multiplied by 1,000. For example, enter 120 for CLS 0.120 and use the same scaling for the variant.
Can I compare lab data with field data?
No. Compare like with like. Lab and field measurements answer different questions and should not be mixed in one calculation.
What user count should I use?
Use the number of users exposed to the variant during the period represented by the conversion and metric data.
Results and formulas
How the displayed outputs are calculated and interpreted.
What does a positive metric improvement mean?
It means the variant metric is lower than the control metric. For the supported metrics, a lower value is treated as better.
How is total user time saved calculated?
The calculator multiplies the metric difference by variant users and divides by 1,000. This is most naturally interpreted as time for LCP and INP.
How is conversion lift calculated?
It divides the conversion-rate difference by the control conversion rate and expresses the result as a percentage.
How is estimated incremental value calculated?
It multiplies estimated incremental conversions by the entered value per conversion.
What if the variant conversion rate is lower?
The conversion lift, incremental conversions, and estimated value become negative, showing an observed decline versus control.
Accuracy and interpretation
Important limits when using test estimates.
Does a better Core Web Vital always increase conversions?
No. Conversion behavior depends on many factors, and an observed association in one test does not establish causation.
Does this calculator check statistical significance?
No. It performs arithmetic from the inputs and does not evaluate sample size, uncertainty, or experiment validity.
Is estimated incremental value a forecast?
No. It is a simple estimate for the measured variant users using observed rates and your entered value per conversion.
Can I use the result to predict a full rollout?
You can use it as a scenario input, but traffic mix, behavior, seasonality, and implementation details may differ after rollout.
How is per-user Core Web Vital improvement calculated?
The calculator subtracts the variant metric from the control metric. A positive difference means the variant has a lower value.
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