Select statistical methods, quantify uncertainty, and validate Monte Carlo/MCMC implementations.
npx skills add https://github.com/dangeles/claude --skill statistician
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
The statistician skill provides expert statistical support to select appropriate methods, quantify uncertainty, and validate simulations in software projects, enabling robust data-driven decisions.
Ask the system to select an appropriate statistical method for a given problem, specify data characteristics, and define analysis goals.
High-intent search queries and answers about installing and using this skill.
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.
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.
Validating Monte Carlo and MCMC implementations involves checking convergence diagnostics and using specific validation criteria. This ensures your simulations produce robust, uncertainty-quantified estimations.
Uncertainty quantification works by calculating confidence and credible intervals alongside sensitivity analyses. This validates how parameter uncertainties impact your overall data-driven decisions.
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.
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.
scooter-lacroixRoute problems to mathematical frameworks using expert heuristics and a decision tree.
the-thinker0Quantify uncertainty with entropy and mutual information measures.
inbharataiExecutes statistical analyses including hypothesis tests, regressions, ANOVA, and power calculations using SciPy and Statsmodels.
xjtulycEstimate physics integrals and thermodynamic observables via Monte Carlo sampling.
