| name | vera-stat-continuous-testing |
| description | Runs distribution diagnostics and primary hypothesis tests for continuous outcome variables. Produces Shapiro-Wilk normality check, skewness, kurtosis, Q-Q plot, and one fully interpreted group comparison (Welch's t for 2 groups or ANOVA with Tukey HSD for 3+ groups) with effect sizes and nonparametric confirmation. Ends with a recommendation block listing Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a continuous/numeric outcome and says "analyze continuous outcome," "my DV is numeric," "compare group means," or names a continuous variable like weight, score, income, time, cost, mpg, blood pressure. Does not handle binary, count, survival, ordinal, repeated measures, or SEM outcomes. |
| user-invocable | true |
| allowed-tools | Read, Bash, Write, Edit |
Continuous Outcome — Distribution Diagnostics & Hypothesis Testing
Open-source skill.
Workflow
Read each step file in workflow/ before executing that step.
| Step | File | Executor | Output |
|---|
| Collect | workflow/01-collect-inputs.md | Main Agent | Structured input summary |
| Diagnose | workflow/02-check-distribution.md | Main Agent | PART 1 code block |
| Test | workflow/03-run-primary-test.md | Main Agent | PART 2-3 code blocks |
Decision Tree
1. CHECK DISTRIBUTION
├── Normal (Shapiro-Wilk p ≥ .05, |skewness| < 1) → parametric primary
└── Non-normal → nonparametric primary + recommend QR/trees
2. GROUP COMPARISON
├── 2 groups → Welch's t + Cohen's d + Mann-Whitney U
└── 3+ groups → ANOVA + η² + Tukey HSD + Kruskal-Wallis
Required Inputs
| Role | What to collect |
|---|
| Outcome (Y) | Variable name, units, what it measures |
| Group variable | What defines groups, how many levels |
| Predictors | For recommendation block (not executed) |
| Covariates | For recommendation block (not executed) |
Code Structure
PART 0: Setup & Data Loading
PART 1: Distribution Diagnostics → plot_01_distribution.png
PART 2: Primary Hypothesis Test → plot_02_boxplot_[var].png
PART 3: Recommendation Block → text listing additional analyses available
Reporting Standards
- p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
- Effect sizes: Cohen's d (t-test), η² (ANOVA) — always alongside p
- 95% CIs: always for mean differences
- Degrees of freedom: always with t and F statistics
- Sample size: final analytic N
- Decimal places: 2 for M/SD, 3 for p and effect sizes
- Non-significance: "not statistically significant at α = .05" — never "no effect"
Hypothesis Tests
| Scenario | Normal | Non-Normal |
|---|
| 2 independent groups | Welch's t | Mann-Whitney U |
| 3+ independent groups | ANOVA + Tukey HSD | Kruskal-Wallis + Dunn's |
Paired/repeated designs → vera-stat-repeated-testing.
Example Dataset
R built-in mtcars: outcome = mpg, 2-group = am, 3+ group = cyl.
Python: sm.datasets.get_rdataset("mtcars").data (with offline fallback to bundled examples/mtcars.csv).
Cross-Skill Interface
Output:
├── code_r → .R script
├── code_python → .py script
├── figures/ → 2 PNGs (distribution + boxplot)
└── recommendations → text block (additional analyses available)
