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multiverse-analysis

Run multiverse and specification curve analyses across analytical decisions.

Updated Aug 23, 2026
Search Tags:#robustness#model specification#multiverse analysis#specification curve#researcher degrees of freedom#decision classification#computational failures
One-click install
npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill multiverse-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multiverse-analysis
Source: https://github.com/Yuuqq/claude-social-science-skills/tree/main/social-science-skills/multiverse-analysis
Command: npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill multiverse-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Multiverse analysis prevents overconfidence by showing how your conclusions change when you vary defensible analytical decisions, making researcher degrees of freedom visible rather than hidden.

Core Features & Use Cases

  • Decision inventory & mapping: Identify selectional, operationalizational, statistical, and computational decision points in an end-to-end research pipeline.
  • Type E/N/U classification: Classify decisions as Type E (equivalent), Type N (non-equivalent), or Type U (uncertain) to avoid contaminating the multiverse with wrong estimands or incomparable questions.
  • Multiverse execution & specification curves: Run many specifications, summarize convergence/failures, and produce a specification curve (Simonsohn, Simmons & Nelson, 2020) to communicate robustness transparently.
  • Mini-multiverse for expensive pipelines: Use a targeted subset when computational cost is high (e.g., LLMs, Bayesian models, networks/ERGM).
  • Computational failure detection: Explicitly report which decision combinations fail to converge so failures become part of the evidence.

Quick Start

Run the provided demo to generate a Type E multiverse, print a specification-curve style table, and observe how changing decisions can flip robustness verdicts.

Frequently Asked Questions about multiverse-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is specification curve analysis and how does it quantify robustness?▼

A multiverse analysis executes all defensible analytical specifications in parallel to reveal how researcher degrees of freedom affect conclusions. It requires decision inventory and Type E/N/U classification to ensure comparable estimands before executing variations.

How do I run multiverse analysis on computationally expensive Bayesian models?▼

Type E (equivalent), Type N (non-equivalent), and Type U (uncertain) classifications prevent contaminating the multiverse with wrong estimands. Classify decisions before execution to ensure specification curve comparisons remain logically valid.

How do I handle convergence failures when running many model specifications?▼

Computational failure detection systematically logs which decision combinations fail to converge during multiverse execution. These failures become part of the evidence, reporting robustness transparently rather than silently dropping non-converging models.

Can I use specification curve analysis for text preprocessing alternatives in computational social science?▼

Specification curve analysis applies to computational social science pipelines including text and ML preprocessing alternatives. It maps operationalizational and statistical decision points, quantifying how varying preprocessing steps impacts research conclusions.

Do I need pandas and numpy to perform multiverse analysis on my research pipeline?▼

Yes, multiverse analysis requires numpy, pandas, and scipy as dependencies. These libraries handle the computational execution across many specifications, data manipulation for decision inventories, and statistical calculations needed for robustness checks.

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