aibot88 Count 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 COUNT TYPE
├── Exposure variable exists → rate model path (offset = log(exposure))
└── No exposure variable → count model path
2. CHECK DISTRIBUTION
├── Variance ≈ Mean (ratio < 1.5) → Poisson
├── Variance >> Mean (ratio ≥ 1.5) → Negative Binomial
└── Excess zeros (>20%) → flag for Zero-Inflated models
3. GROUP COMPARISON
├── 2 groups → Poisson/NB rate test + IRR + Mann-Whitney U
└── 3+ groups → Poisson/NB regression with group factor + LR test + Kruskal-Wallis
Required Inputs
| Role | What to collect |
|---|---|
| Outcome (Y) | Variable name, what it counts, units |
| Group variable | What defines groups, how many levels |
| Exposure/offset | Time at risk, population, area — or none |
| 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_count_distribution.png
PART 2: Primary Hypothesis Test → plot_02_mean_counts_[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: incidence rate ratio (IRR) with 95% CI — always alongside p
- Overdispersion: report variance/mean ratio with interpretation
- Rate: "X.XX events per [unit] of exposure" when applicable
- Degrees of freedom: always with chi-square and LR test statistics
- Sample size: final analytic N
- Decimal places: 2 for means/SDs, 3 for p and IRR, 2 for rate ratios
- Non-significance: "not statistically significant at α = .05" — never "no effect"
Hypothesis Tests
| Scenario | Equidispersed | Overdispersed |
|---|---|---|
| 2 independent groups | Poisson rate test | Negative binomial test |
| 3+ independent groups | Poisson regression + LR test | NB regression + LR test |
Nonparametric confirmation: Mann-Whitney U (2 groups) or Kruskal-Wallis (3+).
Paired/repeated designs → vera-stat-repeated-testing.
Example Dataset
R built-in warpbreaks: outcome = breaks (count), predictors: wool (A/B), tension (L/M/H).
Python: sm.datasets.get_rdataset("warpbreaks").data (with offline fallback to bundled examples/warpbreaks.csv).
Cross-Skill Interface
Output:
├── code_r → .R script
├── code_python → .py script
├── figures/ → 2 PNGs (count distribution + mean counts bar chart)
└── recommendations → text block (additional analyses available)