Recommended reading

• Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.). A clear, concept-first introduction to distributions, variability, and summaries—excellent for intuition and real-data thinking.

• Tukey, J. W. (1977). Exploratory Data Analysis. The classic source for boxplots, robust summaries, and EDA philosophy—foundational for this chapter’s emphasis on describing data.

• Cleveland, W. S. (1993). Visualizing Data. A practical, graphics-first treatment of distributions and patterns; pairs well with histograms, density plots, and eCDFs.

• Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (1983). Understanding Robust and Exploratory Data Analysis. Wiley. A classic edited volume aimed at explaining robust methods in an accessible style; many chapters are practical.

• Wilcox, R. R. (2017). Introduction to Robust Estimation and Hypothesis Testing (4th ed.). Academic Press. A practical, applied text with many worked examples. It starts gently, with chapters on medians, trimmed means, MAD, and robust alternatives to SD.

• Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. More advanced, but the opening chapters give a rigorous yet readable account of why robustness matters. Useful for readers that want to go a bit deeper.