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HaoxuanLiTHUAI
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Cognitive Science Power Analysis

Provide modality-aware power analysis guidance for cognitive science studies.

34|5|Updated Feb 28, 2026
Search Tags:#neuroscience#cognitive-science#sample-size#effect-size#experimental-design#power-analysis#statistical-planning
One-click install
npx skills add https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills --skill cognitive-science-power-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Cognitive Science Power Analysis
Source: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/tree/main/skills/cogsci-power-analysis
Command: npx skills add https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills --skill cognitive-science-power-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides domain-specific power analysis guidance for cognitive science and neuroscience, incorporating modality-aware effect size priors and sample size recommendations to improve study planning.

Core Features & Use Cases

  • Modality-specific priors: Customize effect size priors and sample sizes for behavioral, EEG/ERP, fMRI, and clinical studies.
  • Power-analysis workflows: Analytic solutions for simple designs and simulation-based approaches using tools like SIMR, Superpower, fMRIpower, and NeuroPowerTools.
  • Preregistration-ready reporting: Includes a structured power analysis reporting template and best-practice pitfalls to avoid.
  • Reference libraries: Access curated effect-size libraries and detailed sample-size guidelines by modality.
  • Example use: When designing a new fMRI study, the skill guides the researcher to select priors and determine participant numbers per group.

Quick Start

Define your study design and run a power analysis using modality-specific priors and recommended tools.

Frequently Asked Questions about Cognitive Science Power Analysis

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

FAQPage Schema
How do I calculate sample size for an fMRI study using power analysis?▼

Power analysis for fMRI studies requires modality-specific effect size priors to calculate sample size, utilizing simulation-based workflows with tools like fMRIpower or NeuroPowerTools to estimate participant numbers per group.

What is a simulation-based power analysis and when do I need it for EEG experimental design?▼

Simulation-based power analysis estimates sample size by modeling data generation for complex EEG/ERP designs, needed when analytic solutions are unavailable, often using packages like SIMR to evaluate statistical power across trials.

How do I select appropriate effect size priors for clinical cognitive neuroscience studies?▼

Selecting effect size priors for clinical cognitive neuroscience involves accessing curated reference libraries to match historical data with your specific modality, ensuring accurate sample size recommendations and robust experimental design.

Can I use analytic power analysis workflows for behavioral cognitive science experiments?▼

Yes, analytic power analysis workflows support simple behavioral cognitive science designs by providing direct mathematical solutions for sample size estimation, while simulation-based approaches are recommended for complex experimental structures.

Does this power analysis guidance include templates for study preregistration?▼

Yes, the power analysis guidance includes a structured reporting template specifically designed for preregistration, outlining best-practice pitfalls to avoid and ensuring statistical planning transparency for cognitive science studies.

What are the limitations of using standard statistical software for neuroscience sample size estimation?▼

Standard statistical software often lacks modality-aware priors for neuroscience sample size estimation, making safe integration with specialized power analysis workflows and curated effect-size libraries necessary for valid experimental design.

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