Data Analysis

To create a data analysis report, use a structured markdown template covering executive summaries, methodology, findings, and recommendations. This ensures raw datasets translate into clear, reproducible business intelligence outputs.

For A/B test evaluation, use a statistical test decision tree to select appropriate methods like t-tests, ANOVA, chi-square, or non-parametric alternatives based on your data distribution and hypothesis design.

Cohort analysis requires SQL query patterns utilizing aggregation, window functions, and CTE-based pipelines. These ready-to-use queries help structure complex data transformations for performance tracking.

Ensure data quality by applying a step-by-step checklist verifying completeness, accuracy, consistency, freshness, and outlier handling before applying statistical methods or generating final reports.

Yes, you can apply these data analysis techniques to business intelligence tasks like performance tracking, cohort analysis, and A/B test evaluation, turning raw data into actionable insights quickly.

The best way to structure an A/B test framework is by following a template for hypothesis definition, experimental design, results analysis, and actionable recommendations to ensure rigorous evaluation.