Interpret statistical results correctly and comprehensively. Use when: (1) Writing results sections, (2) Discussing findings, (3) Avoiding common misinterpretations, (4) Reporting effect sizes and confidence intervals.
View on GitHubastoreyai/ai_scientist
research-assistant
January 20, 2026
Select agents to install to:
npx add-skill https://github.com/astoreyai/ai_scientist/blob/main/skills/results-interpretation/SKILL.md -a claude-code --skill results-interpretationInstallation paths:
.claude/skills/results-interpretation/# Results Interpretation Skill ## Purpose Correctly interpret and report statistical findings with appropriate nuance. ## Key Principles **1. Effect Size > p-value** - Report effect sizes with 95% CI - Statistical significance ≠ practical importance **2. Confidence Intervals** - Range of plausible values - Precision of estimate - If CI includes 0, not statistically significant **3. P-values** - Probability of data given H0 - NOT: Probability H0 is true - NOT: Probability of replication **4. Multiple Comparisons** - Adjust alpha if running many tests - Distinguish primary vs exploratory ## Correct Reporting **Example:** "The intervention group showed higher scores (M=45.2, SD=8.3) than control (M=37.8, SD=9.1), t(98)=3.45, p<.001, d=0.69, 95% CI[0.29, 1.09]. This represents a medium-to-large effect." **Include:** - Descriptive statistics - Test statistic and df - P-value - Effect size with CI - Interpretation --- **Version:** 1.0.0