Can you ship this experiment?
A statistically significant number is only one part of a trustworthy A/B test. Enter a result snapshot and this local reviewer checks the data, the OEC, the guardrails, and the conditions around your decision.
Reviewing the evidence
Update the inputs to see whether this result is clear enough to act on.
The recommendation combines the four review gates below.
- Control rate
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- Treatment rate
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- Absolute lift
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- Relative lift
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- P-value
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- 95% delta CI
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Checking the observed allocation.
Checking the Treatment minus Control delta.
Checking for unintended regressions.
Checking duration and the trust checklist.
The important interval is Treatment minus Control. But a clear delta still needs trustworthy assignment, a meaningful OEC, safe guardrails, and a pre-agreed stopping rule.
Term guide
Plain English- OEC
- The Overall Evaluation Criterion: the primary, pre-agreed measure of product or customer value.
- Guardrail
- A metric that should not materially regress, such as latency, crashes, or abandonment.
- SRM
- Sample Ratio Mismatch: observed group sizes differ from the planned allocation enough to question validity.
- Control · A
- The original version, used as the baseline for comparison.
- Treatment · B
- The new version or change being tested against Control.
- Conversion rate
- Conversions divided by users. For example, 100 / 1,000 = 10%.
- Delta
- Treatment rate minus Control rate. A positive delta means B is higher.
- Absolute lift
- The delta expressed in percentage points, such as +2 percentage points.
- Relative lift
- The delta divided by Control. A move from 10% to 12% is a 20% relative lift.
- 95% delta CI
- The uncertainty range for Treatment minus Control. If it includes 0, the direction is uncertain at this level.
- P-value
- How surprising this difference would be if the two versions truly had the same rate.
- Two-proportion z-test
- The approximate statistical method used here to compare two binary conversion rates.
- Novelty / primacy
- A treatment effect that changes over time because users learn, adapt, or react to a new experience.
- Early peeking
- Looking repeatedly and stopping when a result crosses a threshold without accounting for those repeated looks.
Results are calculated locally in your browser. No experiment data is sent anywhere. Inspired by Ron Kohavi's work on trustworthy online controlled experiments.