What is the primary benefit of the Falsification Phase?

It helps you define exactly what result would prove your hypothesis wrong before you start testing, preventing confirmation bias and ensuring scientific integrity.

How does this skill improve A/B testing?

It guides you through variable mapping (independent, dependent, and controls) and prediction generation to ensure your experiments are statistically valid and yield actionable data.

Can I use this for debugging software?

Yes, it is highly effective for complex root-cause analysis by helping you systematically formulate and falsify 'if-then' statements about system behavior.

Does it support different research designs?

Yes, the skill includes logic for Randomized Controlled Trials (RCT), quasi-experiments, longitudinal studies, and cross-sectional designs based on your specific constraints.

What is the Hypothesis Testing skill for Claude Code?

It is a specialized capability that provides a structured framework for scientific inquiry, helping you build testable hypotheses, identify variables, and design valid experiments.

Hypothesis Testing & Experiment Design

Name: Hypothesis Testing & Experiment Design
Author: poemswe

bypoemswe

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データサイエンスとML

Standardizes the scientific research process by guiding users through rigorous hypothesis formulation, experimental design, and variable mapping.

概要

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.

主な機能

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

ユースケース

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

概要

主な機能

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

ユースケース

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