# Stat Result Validator > Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency. - Skill: `gabrielmoreira/stat-result-validator` (Agent Skill) - Install (CLI): `npx skillmds@latest add gabrielmoreira/stat-result-validator` - Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/stat-result-validator/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/gabrielmoreira/stat-result-validator --- # Stat Result Validator ## Overview Use this skill after formulation, method proposal, theory, experimental evaluation, comparison, and result synthesis. It checks whether the final result is supported by a coherent statistical research chain. ## Artifact Checks Required for all topics: ```text progress//step0_problem_formulation.md progress//step1_method_proposal.md progress//step2_theory_analysis.md progress//step3_experimental_evaluation.md progress//step4_comparison.md progress//step5_result_synthesis.md progress//step6_quality_audit.md experiments//config.yaml experiments//results/metrics.json experiments//results/run_manifest.json experiments//results/comparison_summary.md experiments//results/claim_verdicts.json experiments//report/paper.md experiments//README.md ``` Analysis-specific source files and raw outputs are determined by the experiment plan and should live under `experiments//src/` and `experiments//results/`. ## Formulation Checks The formulation must define: - Observed data and sampling regime - Data model or data source - Target parameter, decision, prediction, or risk - Assumptions - Claims or hypotheses - Evaluation criteria - Theory targets Blocking failures: - No target or estimand. - Claims cannot be measured. - Assumptions are absent or incompatible with the proposed method. - Evaluation criteria do not answer the research question. ## Method Checks Verify that: - The proposed method addresses the formulated target. - Baselines are meaningful. - Ablations isolate important design choices. - Diagnostics are specified for likely failure modes. - Method outputs match the metrics and theory targets. ## Theory Checks Theory may be rigorous or partial, but it must be explicit. Check for: - Definitions and assumptions. - Proposition, theorem, derivation, counterexample, or clearly labeled heuristic analysis. - Predicted empirical patterns. - Limitations and regimes not covered. Blocking failures: - No theory section at all. - Theory analyzes a different target than the formulation. - Final claims are stronger than the theory supports. ## Experimental Evidence Checks Check that: - Experiments test the formulated claims. - Conditions are aligned with assumptions and stress tests. - Proposed method, baselines, and ablations are run on comparable conditions. - Failure counts are recorded. - Runtime reductions are recorded in `run_manifest.json`. - Metrics are grouped at the right level. ## Comparison Checks Verify that: - Comparisons include proposed method vs baseline. - Ablations are interpreted. - Experimental patterns are compared with theoretical predictions. - Disagreements between theory and experiments are discussed. - Claim verdicts cite both theoretical and empirical support when available. ## Final Claim Checks Every final claim must be traceable to: ```text formulation -> method -> theory -> experiment -> comparison ``` Use: - `PASS`: formulation, theory, experiments, and comparisons support the claims. - `WARN`: usable but has limitations that must be disclosed. - `FAIL`: missing formulation, theory, evidence, or fair comparison prevents a valid conclusion.