vera-data-continuous-reviewing

name	vera-data-continuous-reviewing
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.
allowed-tools	Read, Bash, Write, Edit

Continuous Outcome — Distribution Diagnostics & Hypothesis Testing

Table of Contents

• Scope Boundary
• Workflow
• Decision Tree
• Required Inputs
• Code Structure
• Reporting Standards
• Hypothesis Tests
• Example Dataset
• Method Status
• Minimal Smoke Test
• Cross-Skill Interface

Open-source skill.

Scope Boundary

Use this skill when:

• The outcome is a single continuous variable and the first need is a transparent baseline comparison across groups.
• A primary t-test / ANOVA-style analysis is appropriate before regression or nonlinear exploratory work.

Do not use this skill when:

• The design is repeated / paired, multivariate, or time-indexed.
• The outcome is binary, count, survival, ordinal, or SEM-based rather than continuous.

Workflow

Read each step file in workflow/ before executing that step.

Step	Responsibility	Executor	Document	Input	Output
Collect	Collect Inputs	Main Agent	workflow/step01-collect-inputs.md	User input	Structured input summary
Diagnose	Check Distribution	Main Agent	workflow/step02-check-distribution.md	Prior step output	PART 1 code block
Test	Run Primary Test	Main Agent	workflow/step03-run-primary-test.md	Prior step output	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

1. p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
2. Effect sizes: Cohen's d (t-test), η² (ANOVA) — always alongside p
3. 95% CIs: always for mean differences
4. Degrees of freedom: always with t and F statistics
5. Sample size: final analytic N
6. Decimal places: 2 for M/SD, 3 for p and effect sizes
7. Non-significance: "not statistically significant at α = .05" — never "no effect"
8. Normality check: Shapiro–Wilk W, p on residuals (not Y directly) for t-test and ANOVA; also report skewness and kurtosis. With large samples (N > 200), Shapiro–Wilk is over-sensitive — trivial deviations from normality reach significance. When N > 200, rely primarily on visual inspection (Q–Q plot) and skewness/kurtosis magnitudes; treat Shapiro–Wilk p as a supporting diagnostic, not a gatekeeper.
9. Variance homogeneity: Levene's test (F, p) before choosing Student's t vs Welch's t or pooled ANOVA vs Welch ANOVA. Default to Welch's t (robust to unequal variances) unless Levene p ≥ .05 and sample sizes are balanced.

Hypothesis Tests

Scenario	Normal (equal var)	Normal (unequal var)	Non-Normal
2 independent groups	Student's t + Tukey HSD (optional)	Welch's t (default)	Mann-Whitney U
3+ independent groups	ANOVA + Tukey HSD	Welch ANOVA + Games-Howell post-hoc	Kruskal-Wallis + Dunn's

When Levene's test indicates heterogeneous variances across 3+ groups, use Welch ANOVA for the omnibus test and Games-Howell for pairwise comparisons — Tukey HSD assumes equal variances and inflates Type I error when variances differ. Report which post-hoc was used and why.

Paired/repeated designs → vera-data-repeated-reviewing.

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).

Method Status

Status	Methods
Implemented in this skill	Residual normality diagnostics, Levene-informed Welch / ANOVA branching, post-hoc tests, and nonparametric confirmation
Implemented downstream in vera-data-continuous-generating	Extended group comparisons, OLS, quantile regression, subgroup analysis, and exploratory tree-based models
Out of scope in this open-source baseline	Repeated-measures, mixed-effects, and any continuous-outcome workflow that depends on a different data structure

Minimal Smoke Test

• Smoke-test prompt: "Run vera-data-continuous-reviewing on mtcars, using mpg as the outcome and am as the primary grouping variable. Produce the standard baseline artifacts."

Cross-Skill Interface

Output:
├── code_r      → .R script
├── code_python → .py script
├── figures/    → 2 PNGs (distribution + boxplot)
└── recommendations → text block (additional analyses available)

Next step: Invoke vera-data-continuous-generating from this skillset to run the full pipeline (additional tests, subgroup analysis, modeling, manuscript generation). See ../../CROSS-SKILL-INTERFACE.md for the shared handoff contract.