Marketing ROI Engine
Check if your A/B test results are statistically significant and estimate sample size needed for reliable conclusions. Two-tailed z-test with configurable confidence level.
Control (A) vs test (B) conversion rates.
Recommendation
Not significant yet. Continue the test or increase traffic to reach a reliable conclusion.
Disclaimer: This calculator uses a two-tailed z-test for proportions. It assumes independent samples and fixed sample sizes. For sequential testing, multi-armed bandits, or tests with multiple comparisons, use a dedicated experimentation platform.
Contract, discovery endpoints, and developer notes for agent use.
Always available for agents
Tool contract JSON
https://aibizhub.io/contracts/ab-test-significance-calculator.jsonStable input and output contract for this exact tool.
Human review
People can use the browser page to sense-check outputs and charts, but agents should still execute against the contract and discovery endpoints.
{
"tool": "ab_test_significance",
"visitors_a": 5000,
"conversions_a": 250,
"visitors_b": 5000,
"conversions_b": 285,
"confidence_level": 95
} No. Start with /agent-tools.json, then follow the tool's contract URL. The page UI is for human review, not parameter discovery.
Every tool opens in Quick Start first. Advanced Controls keeps the same scenario, reveals more assumptions or diagnostics, and every tool keeps AI integrations inline below the instructions.
Open it when a human wants to sense-check the output, review the chart, or keep exploring related tools after the calculation finishes.
It depends on your baseline conversion rate and minimum detectable effect. To detect a 20% relative improvement on a 5% baseline (from 5% to 6%) at 95% confidence and 80% statistical power, you need approximately 4,800 visitors per variant. Smaller effects require dramatically larger samples — detecting a 10% relative improvement requires roughly 19,000 per variant.
A test can be statistically significant (very unlikely to be due to chance) but practically insignificant (effect too small to matter). A 0.1% conversion rate improvement may be p<0.01 with 500,000 visitors but generate only $200/month in additional revenue. Always evaluate effect size alongside p-value — significance without magnitude is misleading.
Run for at least 1-2 full business cycles (usually 2-4 weeks minimum) regardless of when significance is reached. Stopping early when significance appears inflates false positive rates significantly — the peaking-at-significance problem can produce 30-50% of results that fail to replicate. Pre-specify sample size before launching.
Related Resources
Every link here is tied directly to A/B Test Significance Calculator. Use the explanation, formula, examples, and benchmarks to pressure-test the calculator output from first principles.
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