The A/B Test Post-Analysis Calculator evaluates completed experiments to determine statistical significance, effect size, and confidence intervals. It provides actionable insights for shipping or iterating. The formula is Confidence Interval = Observed Difference +/- (Z x Standard Error). Review the chosen significance threshold, effect size, confidence interval, data quality and implementation costs together before rollout. PM Toolkit's free A/B test post-analysis calculator helps product managers evaluate experiments with two-sided frequentist tests and effect size estimation.
What is A/B Test Post-Analysis?
Post-analysis evaluates completed A/B test results to determine statistical significance, calculate confidence intervals, and provide actionable recommendations. It goes beyond simple win/loss to quantify the expected impact of shipping a change.
Key Formulas
Relative Lift = ((Variant Rate - Control Rate) / Control Rate) x 100%
Binary absolute-effect interval = (p_variant - p_control) +/- Z x sqrt(p_control(1-p_control)/n_control + p_variant(1-p_variant)/n_variant)
Projected annual conversion revenue = relative lift x control conversion rate x monthly visitors x value per conversion x 12
Decision Framework
| Scenario | Action | Next Step |
|---|---|---|
| Significant positive result | Consider rollout after quality checks | Monitor for regression |
| Significant negative result | Do not ship | Iterate on hypothesis |
| Inconclusive result | Review uncertainty and the test plan | Do not extend solely to chase significance |
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Post-test Analysis
Review effect estimates, statistical uncertainty, data-quality checks and projected conversion revenue.
Updated
Enter your experiment results to get statistical significance, decision recommendations, and business impact projections with plain-English explanations.
Post-Experiment Analysis
Two-sided analysis with data-quality checks and optional conversion-revenue projections
Experiment Details
Variant Configuration
Primary Metric
Control Group
Variant Group
Test Configuration
Business Context
Optionalto
Revenue projection is available for binary conversion tests only.
P-values do not measure the probability that an effect is real. Read the ASA guidance on p-values and Microsoft’s SRM guidance. Runtime, peeking and recommendation ratings are tool review rules, not guarantees of validity.
What is A/B Test Post-Analysis?
A/B test post-analysis uses the effect estimate, p-value, and confidence interval to assess a completed experiment. Check the planned sample and guardrail metrics as part of the decision about whether to ship.
Decision Rule
Assess effect size, uncertainty, guardrails, and the planned stopping rule
Statistical interpretation
A 95% interval for the difference that excludes zero rejects the no-difference hypothesis in the corresponding two-sided test. It does not establish practical value.
NIST: confidence intervals and corresponding testsRate this calculator:
Common questions
When can I stop an A/B test and declare a winner?
For a fixed-horizon test, stop at the planned endpoint and evaluate the result, even if it is inconclusive. Statistical significance alone does not make a variant worth shipping. Check effect size, uncertainty, guardrails, and implementation costs. Repeated interim checks require a method designed for them.
What is statistical significance in A/B testing?
Statistical significance means the observed difference between control and variant is unlikely if the two were actually identical. A p-value below 0.05 means that, if there were truly no difference, you would see a result this extreme less than 5% of the time. The post-analysis calculator estimates p-values and confidence intervals under the selected test assumptions.
How do I interpret A/B test confidence intervals?
A confidence interval shows the estimate’s uncertainty under the test assumptions. A method producing 95% intervals covers the true effect in 95% of repeated samples. For a matching two-sided test, an interval excluding zero corresponds to significance at the associated level. Check whether the interval includes effects too small to matter.
What is practical significance vs statistical significance?
Statistical significance assesses evidence against the null hypothesis under the test assumptions. Practical significance asks whether the size of the effect matters for the decision. A small effect can be statistically significant with enough data but still cost more to implement than it returns.