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experimental-design

Design experiments with randomization, power analysis, and validity threat mitigations.

Updated Aug 27, 2026
Search Tags:#randomization#power analysis#pre-registration#experimental design#RCT#field experiment#balance diagnostics
One-click install
npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill experimental-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experimental-design
Source: https://github.com/Yuuqq/claude-social-science-skills/tree/main/social-science-skills/experimental-design
Command: npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill experimental-design

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Experimental research often fails when treatment assignment, sample size, or validity threats are poorly specified, leading to biased estimates and underpowered results.

Core Features & Use Cases

  • Experimental & quasi-experimental design guidance: Choose between lab, field, survey, natural experiments, and A/B testing while accounting for internal/external validity tradeoffs.
  • Power analysis and MDE planning: Compute required sample sizes, minimum detectable effects, and cluster-randomized design effects to avoid underpowered studies.
  • Randomization, balance diagnostics, and validity threats: Implement simple/stratified/cluster randomization and assess balance on observables; address attrition, noncompliance, SUTVA/spillovers, multiple testing, and common reporting pitfalls.
  • Analysis framework and pre-registration structure: Use ITT as a primary approach, plan robustness/heterogeneity checks, and prepare a pre-registration outline for repositories like OSF.

Quick Start

Use the experimental-design skill to produce a full RCT or field experiment plan with power analysis, a randomization procedure, balance diagnostics, and a pre-registration-ready analysis outline.

Frequently Asked Questions about experimental-design

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

FAQPage Schema
How do I calculate sample size and minimum detectable effects for an RCT?▼

Yes, you can plan cluster-randomized field experiments by specifying stratified randomization, computing design effects for intraclass correlation, and generating balance diagnostics requirements to ensure valid causal inference.

How do I design a randomization strategy and check balance for a field experiment?▼

To design a randomization strategy and check balance for a field experiment, you select a simple, stratified, or cluster randomization approach and run balance diagnostics on observables to mitigate validity threats.

Can I generate a pre-registration outline for an experimental design?▼

Yes, you can generate a pre-registration outline by structuring an intention-to-treat (ITT) centered analysis framework, defining robustness checks, and preparing the reporting plan for repositories like OSF.

How do I plan for attrition and noncompliance in A/B testing?▼

To plan for attrition and noncompliance in A/B testing, you address these validity threats during the experimental design phase by outlining an ITT-centered analysis framework that preserves assignment integrity.

Does this experimental design approach work for survey experiments and natural experiments?▼

Yes, this experimental design approach works for survey experiments and natural experiments by evaluating internal and external validity tradeoffs while applying randomization and quasi-experimental design guidance.

What is the best way to address SUTVA and spillovers in experimental design?▼

The best way to address SUTVA and spillovers in experimental design is to identify these validity threats during planning and specify randomization strategies that minimize interference across treatment units.

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