statistical-method-design
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
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- aiming-lab/AutoResearchClaw
- Última actividad en el origen
- 20 de mayo de 2026 a las 04:39
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Instrucciones de origen · Vista previa de solo lectura- name
- statistical-method-design
- description
- Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
- metadata
- {"category":"domain","trigger-keywords":"method proposal,estimator,algorithm,baseline,ablation,diagnostic,statistical method","applicable-stages":"3,4,5,6,7,8","priority":"1"}
# 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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