Data Visualization › Summary

Visualization gives intuitive understanding of data that precedes and informs all subsequent analysis. Histograms reveal distributional properties like shape, center, spread, and the presence of multiple modes or extreme skewness. Box plots concisely summarize key statistics while allowing efficient comparisons across groups and showing potential outliers. Scatter plots expose relationships between variables.

The practice of looking at data before computing statistics is not mere convention but important discipline. Visual patterns guide the selection of appropriate statistical methods, reveal violations of modeling assumptions, and expose data quality issues that could invalidate formal analyses. While visualization is inherently subjective and should be complemented by rigorous statistical testing, it remains a foundational step in transforming raw data into reliable insight. By developing strong visualization habits and cultivating visual intuition, practitioners build the basis for all subsequent analytical work.