statistician

Select statistical methods, quantify uncertainty, and validate Monte Carlo/MCMC implementations.

6|Updated Feb 3, 2026

Search Tags:#simulation #hypothesis testing #statistics #uncertainty #power analysis #Bayesian #MCMC

Ready to run

npx skills add https://github.com/dangeles/claude --skill statistician

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistician
Source: https://github.com/dangeles/claude/tree/main/claude-config/skills/statistician
Command: npx skills add https://github.com/dangeles/claude --skill statistician

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The statistician skill provides expert statistical support to select appropriate methods, quantify uncertainty, and validate simulations in software projects, enabling robust data-driven decisions.

Core Features & Use Cases

Method selection for experimental design and data analysis
Power analysis and sample size calculations for reliable conclusions
MCMC/Monte Carlo validation guidance and convergence diagnostics
Uncertainty quantification through confidence/credible intervals and sensitivity analyses
Real-world use: designing an A/B test with power constraints, validating Monte Carlo estimations in a simulation, and assessing uncertainties in model parameters.

Quick Start

Ask the system to select an appropriate statistical method for a given problem, specify data characteristics, and define analysis goals.

Frequently Asked Questions about statistician

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

FAQPage Schema

How do I select the right statistical method for software project data analysis?▼

To select the right statistical method, you need to provide the problem context, data characteristics, and analysis goals. This yields method recommendations, validation criteria, and implementation guidance tailored to your software project.

What's the best way to calculate sample size and perform power analysis for an A/B test?▼

Power analysis and sample size calculations require defining your analysis goals and data characteristics. This process yields reliable conclusions by ensuring your experimental design has enough power to detect meaningful effects.

How do I validate Monte Carlo simulations and check MCMC convergence?▼

Validating Monte Carlo and MCMC implementations involves checking convergence diagnostics and using specific validation criteria. This ensures your simulations produce robust, uncertainty-quantified estimations.

How does uncertainty quantification work for model parameters in data analytics?▼

Uncertainty quantification works by calculating confidence and credible intervals alongside sensitivity analyses. This validates how parameter uncertainties impact your overall data-driven decisions.

Can I use Bayesian hypothesis testing for software engineering experiments?▼

Yes, Bayesian hypothesis testing applies to software engineering experiments by requiring problem context and data characteristics as inputs. It yields method recommendations and implementation guidance for robust analysis.

When should I not use standard statistical methods for software project data?▼

Standard statistical methods may be insufficient when your software project requires complex uncertainty quantification or Monte Carlo validation. In these cases, advanced method selection and convergence diagnostics are necessary.