# Statistical Method Design

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

- Skill: `gabrielmoreira/statistical-method-design` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/statistical-method-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/statistical-method-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/statistical-method-design

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# 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"
```