Statistical Analysis
Skill Verifiziert AktivGuided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
To assist users in conducting rigorous statistical analyses, choosing appropriate tests, verifying assumptions, and generating professional reports for academic and research purposes.
Funktionen
- Guided statistical test selection
- Automated assumption checking
- Support for hypothesis testing (t-tests, ANOVA, chi-square, regression, Bayesian)
- Effect size calculation and interpretation
- Power analysis for study planning
- APA-formatted statistical reporting
Anwendungsfälle
- Choosing appropriate statistical tests for experimental or observational data
- Checking and addressing violations of statistical assumptions
- Performing hypothesis tests and regression analyses
- Generating publication-quality statistical reports in APA format
- Planning study sample sizes with power analysis
Nicht-Ziele
- Programmatic implementation of specific statistical models using libraries like statsmodels
- Generating complex custom visualizations beyond standard diagnostic plots
- Replacing dedicated statistical software for highly specialized or niche analyses
Workflow
- Select a statistical test based on research question and data
- Check statistical assumptions (normality, homogeneity of variance, etc.)
- Run the appropriate statistical analysis
- Calculate effect sizes and confidence intervals
- Generate APA-style reports and visualizations
Praktiken
- Statistical best practices
- Research methodology
- Data analysis
- Academic reporting
Voraussetzungen
- Python 3.11+ (3.12+ recommended)
- uv (Python package manager)
- Agent supporting Agent Skills standard
- macOS, Linux, or Windows with WSL2
Execution
- info:Pinned dependenciesWhile the repository utilizes `uv` for dependency management, specific pinning details for each skill's dependencies aren't explicitly detailed in the SKILL.md, relying on standard package management practices.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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