Statistical Experimental Evaluation

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.