result-analysis

Research

Statistically analyze collected results, verify reproducibility, and synthesize findings

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How to use this skill

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/result-analysis/SKILL.md

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Strategy: Result Analysis

Key Question: What do the results tell us?

Methodology

Three-layer analysis combining frequentist, resampling, and Bayesian approaches:

Statistical Testing — Bootstrap CI, Permutation tests, Bayesian ROPE judgment
Effect Size Calculation — Cohen's d, Cliff's delta, or domain-appropriate measure
Reproducibility Verification — Re-run with different seeds, compare distributions
Synthesis — Integrate findings into actionable conclusions

Execution Flow

[Collected results from experiment-running]
    → statistical-testing (bootstrap/permutation/Bayesian)
        → effect size calculation
            → reproducibility-verification (re-run, compare)
                → execution-synthesis (comprehensive report)
                    → OUTPUT: validated findings with confidence levels

Budget Gate

Step	Max Budget	Output
Statistical testing	8%	Test results with p-values/CIs
Reproducibility	8%	Re-run comparison
Synthesis	4%	Final report

Key Decisions

Test selection:
- Known distribution → parametric (t-test, ANOVA)
- Unknown/non-normal → bootstrap CI or permutation test
- Need practical significance → Bayesian ROPE
Reproducibility threshold: Results must agree within 1 SE across re-runs
Effect size interpretation:
- Small: d < 0.2 (may not be practically significant)
- Medium: 0.2 ≤ d < 0.8 (likely meaningful)
- Large: d ≥ 0.8 (strong effect)
ROPE (Region of Practical Equivalence): Define before testing, not after

Integration with Knowledge System

Results feed back into:

Wiki vault (claims with evidence)
Future experiment design (what worked, what didn't)
North star progress tracking

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
result-validation-loop	Validate results through statistical testing, ROPE judgment, reproducibility re-runs, and final synthesis

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
execution-synthesis	Synthesize complete execution report from all results, tests, and reproducibility data
reproducibility-verification	Verify result reproducibility via re-runs with different seeds and ICC comparison
statistical-testing	Execute statistical tests — bootstrap, permutation, Bayesian ROPE — on experiment results