Dashboard Design Brief
Write a complete brief for a data dashboard — what to show, how to organize it, and what decisions it should drive.
Interpret statistical output in plain language — what the numbers actually mean and whether your conclusions hold up.
Help me interpret statistical results and communicate what they actually mean. **The statistical output I have:** [Paste your results — e.g. regression coefficients, p-values, confidence intervals, correlation matrix, t-test output, ANOVA table] **The analysis I ran:** [What type of analysis? e.g. linear regression, chi-square test, t-test, A/B test, correlation analysis] **What I was trying to find out:** [The question or hypothesis you were testing] **My audience:** [Who needs to understand this — their statistical background level] **Context:** - Sample size: [How many data points?] - Data source: [How was this collected?] - Any known limitations: [Sampling issues, confounders, measurement problems] Interpret the results by covering: 1. **Plain language summary** — what the results say in 2–3 sentences with no jargon 2. **Statistical significance** — what the p-values or confidence intervals actually mean here (not just "p<0.05 means significant") 3. **Practical significance** — is the effect size meaningful in the real world, even if statistically significant? 4. **What you CAN conclude** — the specific claims the data supports 5. **What you CANNOT conclude** — common over-interpretations to avoid (causation, generalizability) 6. **Limitations** — how the study design or data quality affects confidence in the results 7. **Next steps** — what follow-up analysis or data would strengthen or challenge this conclusion
Interpreting statistical output from analyses, experiments, and research studies — translating numbers into defensible, plain-language conclusions.
A plain-language interpretation covering what the results show, significance vs. practical meaning, valid conclusions, over-interpretation risks, limitations, and recommended next steps.
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Claude Sonnet 4
Write a complete brief for a data dashboard — what to show, how to organize it, and what decisions it should drive.
Design a structured analysis plan — what to measure, how to measure it, and how to avoid common analytical mistakes.
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