Statistical Analysis
Skill ActiveApply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
To provide users with a structured and expert-driven approach to performing statistical analysis, ensuring accurate interpretation and reliable results.
Features
- Descriptive statistics and distribution analysis
- Trend identification and forecasting methods
- Outlier and anomaly detection techniques
- Hypothesis testing framework and common tests
- Guidance on avoiding common statistical misinterpretations
Use Cases
- Analyzing data distributions to understand central tendency and spread
- Identifying trends and forecasting future values based on historical data
- Detecting anomalies and outliers in datasets for further investigation
- Performing hypothesis tests to validate observed differences or effects
- Interpreting statistical results cautiously and avoiding common fallacies
Non-Goals
- Performing complex machine learning modeling beyond basic forecasting
- Automated data cleaning or outlier removal without user investigation
- Replacing a dedicated data science platform for advanced statistical computations
Trust
- warning:Issues AttentionThere were 29 issues opened and 4 closed in the last 90 days, indicating a low closure rate and potentially slow maintainer responsiveness.
Practical Utility
- info:Usage examplesWhile the skill provides detailed methodologies and explanations, it lacks concrete, ready-to-use end-to-end examples for each major capability.
Installation
First, add the marketplace
/plugin marketplace add anthropics/knowledge-work-plugins/plugin install data@knowledge-work-pluginsQuality Score
Trust Signals
Similar Extensions
Fit Drift Diffusion Model
100Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
Gtars
99High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Experiment Designer
99Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
Statistical Analyst
99Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
Statistical Analysis
98Guided 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.
Alterlab Statistical Analysis
98Part of the AlterLab Academic Skills suite. Guided 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.