A/B Test Results Interpreter
Interpret your A/B test results with statistical rigor — determine significance, effect size, and whether to ship the change.
Prompt Template
You are a statistician and growth analyst. Interpret the following A/B test results: Test name: [what was tested] Hypothesis: [expected outcome and why] Variant A (control): [description] Variant B (treatment): [what changed] Results: - Control: [visitors] visitors, [conversions] conversions - Treatment: [visitors] visitors, [conversions] conversions - Test duration: [days] - Confidence level target: [e.g., 95%] Also provide: 1. Conversion rates for each variant 2. Statistical significance (p-value explanation in plain English) 3. Relative uplift % 4. Minimum detectable effect — was the test powered correctly? 5. Ship / don't ship recommendation with reasoning 6. What to test next based on these results
Example Output
A/B Test Results: CTA Button Color Change
**Test:** Blue CTA vs. Green CTA on pricing page
Results Summary
| | Control (Blue) | Treatment (Green) |
|--|--|--|
| Visitors | 4,200 | 4,150 |
| Conversions | 189 | 228 |
| Conv. Rate | 4.50% | 5.49% |
| **Uplift** | — | **+22% relative** |
Statistical Significance
**p-value: 0.008** — This is well below the 0.05 threshold.
In plain English: If the button color truly had no effect, we'd see a difference this large or larger only 0.8% of the time by chance. We can be 99.2% confident this result is real.
Power Analysis
With ~4,000 visitors per variant and a baseline of 4.5%, the test was powered to detect a ~1.2% absolute change. Our observed change (0.99 percentage points) was within range — the test was appropriately sized.
✅ Recommendation: SHIP
The result is statistically significant, practically meaningful (+22% conversions), and the test was properly powered. Ship the green button.
Next Test Ideas
1. Test button copy: 'Start Free Trial' vs. 'Get Started Free'
2. Test button position: above vs. below social proof
3. Test on mobile specifically — segment your results by device type
Tips for Best Results
- 💡Never call a test early because results 'look good' — statistical significance is only valid at the planned end date
- 💡Segment results by device, user type, and traffic source — the overall number often hides important differences
- 💡A 95% confidence level means 1 in 20 'significant' results is a false positive — run tests sequentially, not in parallel
Frequently Asked Questions
What is the A/B Test Results Interpreter prompt?
Interpret your A/B test results with statistical rigor — determine significance, effect size, and whether to ship the change. It's a free ChatGPT prompt template from our Data Analysis collection — copy it, fill in the bracketed variables, and paste it into your AI tool.
Which AI tools work with this prompt?
It's written and tested for ChatGPT and Claude. Any AI assistant that accepts free-form text prompts will handle it well.
How do I customize this ChatGPT prompt?
Replace the bracketed variables — such as [what was tested], [expected outcome and why], [description] — with your own details before running it. Never call a test early because results 'look good' — statistical significance is only valid at the planned end date
Is this prompt free to use?
Yes. Every prompt on PromptAtlas is free to copy, customize, and use — no signup required.
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