Data Cleaning Checklist: 12 Steps Before Statistical Analysis
Reliable analysis begins with a documented audit of structure, meaning, missingness, duplicates, ranges, transformations, and analytical readiness.
Read guide →Data Analysis Guides, Workflows, and Visualization
This collection covers the high-intent tasks analysts face before and after statistical modeling. Use it to clean data, choose visualizations, investigate missingness, and turn analytical output into a clear and reproducible decision trail.
Core answer
A reliable data-analysis workflow defines the decision, audits data quality, documents transformations, explores patterns, applies suitable methods, validates results, and communicates uncertainty clearly.
Which Chart Should I Use? Data Visualization Selection Guide
Choose charts by the question and data structure, not by decoration. Comparisons, distributions, relationships, time, and uncertainty need different forms.
Read guide →How to use this collection
Begin with the guide closest to your immediate decision. Record the assumptions, settings, and definitions you use, then open the linked DataStatPro workflow to apply the method. Educational examples should be adapted to the actual study protocol and data structure.