domains / families / data / data-analysis

data-analysis

combined

Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls. End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures

When to use

• analyzing data
• finding patterns
• understanding metrics
• testing hypotheses
• cohort analysis
• A/B testing
• churn analysis
• statistical significance

When not to use

• proving a predetermined conclusion
• analysis with sample size too small for reliable inference
• analysis with data quality issues that invalidate results
• analysis with uncontrolled confounders

Skill page

Conflict

The two skills share the name 'data-analysis' but describe different procedures. The first is a general statistical methodology guide focused on decision framing, statistical rigor, and avoiding analytical pitfalls. The second is a specific, end-to-end R workflow for empirical research, including file structure, specific packages (fixest, modelsummary), and publication-ready output standards. Neither is a reproduction of the other; they are complementary but distinct approaches to data analysis.