vera-stat-binary-testing
Runs class balance diagnostics and primary association tests for binary outcome variables (0/1, yes/no, survived/died, pass/fail). Produces proportion tables, class balance check with rare-event warning, descriptives by outcome level, chi-square test of independence with Cramer's V, Fisher's exact test when cell counts are small, odds ratio with 95% CI, and a mosaic or grouped bar chart. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a binary/dichotomous outcome and says "binary outcome," "logistic," "survived or died," "yes or no," "pass or fail," "0/1," "classification," "binary DV," or names a binary variable like survived, admitted, defaulted, churned, diagnosed. Does not handle continuous, count, survival time, ordinal, repeated measures, or SEM.
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- vera-stat-binary-testing
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- Runs class balance diagnostics and primary association tests for binary outcome variables (0/1, yes/no, survived/died, pass/fail). Produces proportion tables, class balance check with rare-event warning, descriptives by outcome level, chi-square test of independence with Cramer's V, Fisher's exact test when cell counts are small, odds ratio with 95% CI, and a mosaic or grouped bar chart. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a binary/dichotomous outcome and says "binary outcome," "logistic," "survived or died," "yes or no," "pass or fail," "0/1," "classification," "binary DV," or names a binary variable like survived, admitted, defaulted, churned, diagnosed. Does not handle continuous, count, survival time, ordinal, repeated measures, or SEM.
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# Binary Outcome --- Class Balance Diagnostics & Association 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 CLASS BALANCE
├── Balanced (minority ≥ 10%) → standard tests
└── Imbalanced (minority < 10%) → rare event warning, note power limits
2. PRIMARY ASSOCIATION TEST
├── All cells ≥ 5 → Chi-square + Cramer's V + OR
└── Any cell < 5 → Fisher's exact + OR
```
## Required Inputs
| Role | What to collect |
|---|---|
| **Outcome (Y)** | Variable name, what 0/1 represents |
| **Primary 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: Class Balance Diagnostics → plot_01_class_balance.png
PART 2: Primary Association Test → plot_02_mosaic_[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: Cramer's V (chi-square), odds ratio with 95% CI
3. Odds ratios: always as "OR = X.XX, 95% CI [X.XX, X.XX]"
4. Proportions: report as percentages with 1 decimal
5. Chi-square: chi-sq(df) = X.XX, p = .XXX, Cramer's V = .XXX
6. Degrees of freedom: always with chi-square statistic
7. Sample size: final analytic N
8. Decimal places: 1 for proportions, 2 for OR, 3 for p and effect sizes
9. Non-significance: "not statistically significant at alpha = .05" --- never "no association"
## Association Tests
| Scenario | Expected cells all >= 5 | Any expected cell < 5 |
|---|---|---|
| 2x2 or 2xK | Chi-square + Cramer's V | Fisher's exact test |
Paired/matched binary designs -> `vera-stat-repeated-testing` (handles within-subject comparisons including paired binary data via McNemar's test).
## Example Dataset
R built-in `Titanic` (convert to data frame): outcome = Survived (0/1), predictors = Class, Sex, Age.
Python: `sm.datasets.get_rdataset("Titanic").data` or reconstruct from counts (offline fallback uses base R `Titanic` dataset).
## Cross-Skill Interface
```
Output:
├── code_r → .R script
├── code_python → .py script
├── figures/ → 2 PNGs (class balance + mosaic/grouped bar)
└── recommendations → text block (additional analyses available)
```
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