# Statistical Method Design

> Statistical Method Design is an Agent Skill for research published in the aiming-lab/AutoResearchClaw repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

- Skill name: `statistical-method-design`
- Category: [Research](https://www.openparable.com/skill/research/1)
- Source: https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/stat_research_agent/skills/statistical-method-design/SKILL.md
- Repository: aiming-lab/AutoResearchClaw (13,814 GitHub stars when collected)

## SKILL.md

# Statistical Method Design

## Overview

Use this skill after formal problem formulation. The method should be a response
to the formal target and assumptions, not a generic collection of techniques.

## Required Method Proposal

For each method:

- Name
- Problem it solves
- Formula or algorithm
- Inputs and outputs
- Tuning parameters
- Required assumptions
- Diagnostics
- Expected failure modes
- Computational cost
- Relation to baselines

## Baselines and Ablations

Always define meaningful baselines:

- Classical or standard method
- Naive or unadjusted method
- Oracle or idealized reference when available
- Robust variant
- Ablation removing the key design feature

## Method-to-Claim Map

Every method must connect to at least one claim:

```yaml
method_to_claim_map:
  proposed_method:
    claims: [C1, C2]
    expected_evidence: "lower risk under stress condition"
    theory_target: "consistency under assumptions A1-A3"
```

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Source: https://www.openparable.com/skill/research/statistical-method-design (OpenParable). Cite the canonical URL above.