Statistical Results Interpretation AI Prompt

Interpret and communicate these statistical results for a non-technical audience.

Statistical results: {{results}}
Audience: {{audience}} (business stakeholders, clinical team, policymakers, general public)
Context: {{context}}

1. Lead with the scientific conclusion, not the statistic:
   - Start with what it means for people and decisions, not the p-value
   - Wrong: 'The t-test yielded t(48) = 2.3, p = 0.026'
   - Right: 'Patients receiving the new treatment recovered an average of 3 days faster than controls'

2. Effect size before statistical significance:
   - Report the magnitude of the effect, not just whether it is statistically significant
   - 'The intervention increased sales by 12% (95% CI: 7% to 17%)'
   - A large sample can produce a statistically significant but practically meaningless effect
   - A small sample can fail to detect a large and important effect

3. Confidence intervals over p-values:
   - Report 95% CIs alongside point estimates
   - CI communicates uncertainty: a wide interval means we are less sure about the true effect
   - 'We are 95% confident the true effect is between 7% and 17%'
   - Never say 'the probability that the true value is in this interval is 95%' (frequentist CI does not have this interpretation)

4. Practical significance:
   - Is the effect large enough to matter for the decision at hand?
   - Provide a concrete translation: 'An 8% reduction in churn would save approximately $2M annually'
   - Benchmark against a meaningful threshold, not just 'statistically significant'

5. What statistical significance does and does NOT mean:
   - It means: if the null hypothesis were true, we would rarely see results this extreme by chance
   - It does NOT mean: the effect is large, important, replicable, or clinically meaningful
   - p > 0.05 does NOT mean the null hypothesis is true

6. Uncertainty and limitations:
   - What assumptions could be violated?
   - What alternative explanations cannot be ruled out?
   - How would the interpretation change if the sample were different?

Return: plain-language interpretation of each result, effect size with CI, practical significance assessment, and caveats.