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statistical-experimental-evaluation

Research
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Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

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How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/stat_research_agent/skills/statistical-experimental-evaluation/SKILL.md

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Statistical Experimental Evaluation

Overview

Use this skill after formulation, method proposal, and theory. Experiments should test specific claims and theoretical predictions.

Experiment Plan

Define:

  • Conditions or data-generating processes
  • Real data source or synthetic data generator
  • Sample sizes, folds, repetitions, seeds, or resamples
  • Proposed method
  • Baselines
  • Ablations
  • Diagnostics
  • Metrics
  • Failure accounting

Required Artifacts

experiments/<TOPIC_ID>/config.yaml
experiments/<TOPIC_ID>/src/
experiments/<TOPIC_ID>/results/metrics.json
experiments/<TOPIC_ID>/results/run_manifest.json
experiments/<TOPIC_ID>/results/comparison_summary.md
experiments/<TOPIC_ID>/results/claim_verdicts.json
experiments/<TOPIC_ID>/report/paper.md
experiments/<TOPIC_ID>/README.md

Evidence Schema

Use a row-oriented metric format:

{
  "topic_id": "TXX",
  "metric_rows": [
    {
      "claim_id": "C1",
      "method": "proposed_method",
      "baseline": "standard_method",
      "condition": "stress_condition",
      "metric": "risk",
      "value": 0.12,
      "status": "ok"
    }
  ]
}

Claim verdicts should connect theory and experiments:

[
  {
    "claim_id": "C1",
    "verdict": "supported",
    "theory_support": "Proposition 1 under A1-A3",
    "experimental_support": "Proposed method has lower risk in conditions X-Y",
    "comparison": "Outperforms baseline B on metric M",
    "limitations": "Finite sample only; assumption A2 not tested"
  }
]

Evidence Rules

  • A metric must map to a formulated claim.
  • A comparison must use the same data conditions across methods.
  • Failed runs must be counted.
  • Runtime reductions must be recorded.
  • Results must be interpreted against theoretical predictions.