Hypothesis Testing & Experiment Design

About

This skill transforms Claude into a specialized research assistant that enforces scientific rigor during the investigation of new ideas, data patterns, or system behaviors. It leads you through a structured eight-phase workflow—from initial observation and null hypothesis construction to selecting appropriate experimental designs and defining falsification criteria. Whether you are conducting A/B tests, academic research, or root-cause analysis in complex software systems, this skill ensures your methodology is robust, reproducible, and free from common biases or confounding variables.

Key Features

• Confound identification and mitigation strategy planning
• Selection logic for experimental vs. observational designs
• 4 GitHub stars
• Null and alternative hypothesis (H₀/H₁) construction guidance
• Structured 8-phase scientific research workflow
• Comprehensive variable mapping and operationalization criteria

Use Cases

• Structuring root-cause analysis investigations for complex system bugs
• Formulating testable hypotheses for data science projects and machine learning models
• Designing statistically sound A/B tests for software features and UI changes