statistical-analysis

Quick Review

Exceptional statistical analysis skill with comprehensive coverage of test selection, assumption checking, effect sizes, power analysis, and APA reporting. The description clearly articulates when to use the skill (hypothesis testing, research reporting, assumption checking). Task knowledge is outstanding with detailed code examples, decision trees, and complete workflows for t-tests, ANOVA, regression, and Bayesian methods. Structure is excellent with a clear hierarchical organization, quick reference tables, and logical progression from test selection through reporting. The skill references additional files (test_selection_guide.md, assumptions_and_diagnostics.md, etc.) and a comprehensive assumption_checks.py module, which are assumed present per scoring rules. Novelty is strong as this skill provides significant value over a CLI agent by offering structured guidance for statistical test selection, automated assumption checking with visualizations, effect size interpretation with discipline-specific benchmarks, and APA-formatted reporting templates—tasks that would require extensive prompting and domain expertise. The skill effectively reduces cognitive load and token costs for academic research workflows. Minor improvement opportunity: could include more advanced topics like structural equation modeling or mixed-effects models, though current scope is already comprehensive for its target use cases.