Statistical Analysis Interpreter
The Statistical Analysis Interpreter takes raw statistical output, whether from Python, R, SPSS, Excel, or any other tool, and translates it into plain language explanations with actionable business conclusions. Instead of staring at p-values and confidence intervals wondering what they mean for your decision, you get a clear narrative.
Data analysts who run tests but need to explain results to non-technical stakeholders, product managers evaluating A/B test outcomes, researchers preparing findings for publication, and students learning statistics use this template. It works with any statistical method: t-tests, chi-square, regression, ANOVA, correlation, and more.
The prompt produces superior output because it requires you to share both the raw statistical output and the business question behind it. This context lets the AI connect the numbers to your actual decision. A p-value of 0.03 means nothing in isolation, but "there is strong evidence that the new checkout flow increases conversion, and the estimated lift of 2.1 percentage points would generate approximately $340K in additional annual revenue" is something you can act on.
This prompt is just the starting point
Score it with AI, optimize it with one click, track versions, and build your prompt library.
The Prompt
Interpret the following statistical analysis results:
**Business Question**: [THE QUESTION THIS ANALYSIS ANSWERS, e.g., "Does the new onboarding flow reduce time-to-first-action compared to the current flow?"]
**Statistical Test / Method Used**: [TEST NAME, e.g., "Independent samples t-test" or "Multiple linear regression" or "I'm not sure, please identify it"]
**Tool Used**: [Python/scipy, R, SPSS, Excel, Google Sheets, or other]
**Raw Output**:
```
[PASTE YOUR STATISTICAL OUTPUT HERE, e.g.,
t-statistic: -2.847
degrees of freedom: 198
p-value: 0.0049
Mean Group A (control): 4.7 days
Mean Group B (new flow): 3.2 days
95% CI for difference: [-2.54, -0.46]
Cohen's d: 0.40]
```
**Sample Size**: [TOTAL N AND GROUP SIZES IF APPLICABLE, e.g., "100 per group, 200 total"]
**Additional Context**: [ANY RELEVANT BUSINESS CONTEXT, e.g., "We ran this test for 3 weeks, 10% of traffic was in the test group"]
Provide the following interpretation:
### 1. Plain Language Summary
In 2-3 sentences a non-technical executive could understand, explain what the results mean. Start with the conclusion ("The new flow works" or "There is no meaningful difference"), then support it with the key numbers translated into business terms.
### 2. Statistical Interpretation
Explain each component of the output:
- What each number means (test statistic, p-value, confidence interval, effect size)
- Whether the result is statistically significant at the 0.05 level
- The practical significance: is the effect large enough to matter for the business?
- Direction and magnitude of the effect in real-world units
### 3. Assumptions and Limitations
- List the key assumptions of this test (e.g., normality, independence, equal variance) and whether they are likely met given the sample size and context
- Note any caveats: small sample, multiple comparisons, selection bias risk, or time-period concerns
### 4. Recommendation
Based on the results and context, what should the team do? Be specific:
- Implement the change, run a longer test, test with a larger sample, investigate a confounding variable, or something else
- If the result is inconclusive, estimate the sample size needed to detect the observed effect with 80% power
### 5. How to Present This
Suggest a 1-2 sentence summary suitable for a slide deck or Slack message to stakeholders. Keep it jargon-free and action-oriented.Usage Tips
- Paste the complete output: Include all numbers, not just the p-value. Effect sizes, confidence intervals, and sample sizes are essential for a meaningful interpretation. Partial output leads to partial answers.
- Always state the business question: "Is p < 0.05?" is a statistics question. "Should we ship this feature?" is a business question. The interpreter needs the business question to connect numbers to decisions.
- Say if you are unsure which test was used: If you ran a function without fully understanding the method, mention that. The interpreter will identify the test and note whether it was the right choice for your data.
- Use for learning: After getting the interpretation, ask follow-up questions about any concept you do not understand. This is an effective way to build statistical intuition alongside real analyses.
- Watch for practical vs. statistical significance: A large sample can make tiny differences "statistically significant." The interpreter flags this, but pay special attention to the practical significance section when your sample exceeds 10,000.
Get more from this prompt
Save it, score it with AI, optimize it, and track every version. Free to start.