Chapter 5 Make Meaningful Comparisons
Now that you’ve refined your data story, improved your spreadsheet skills, found and questioned your data, and cleaned up any messy parts, this chapter focuses on the key question to ask while analyzing your evidence: “Compared to what?” That’s how statistician Edward Tufte defined the “heart of quantitative reasoning.”22. We search for insightful findings in our data by judging their significance against each other, to identify those that truly stand out. Sometimes we need to adjust our scales to ensure that we’re weighing data fairly, or as the saying goes, comparing apples to apples, not apples to oranges. Before you communicate your findings in any format—text, tables, charts, or maps—be sure that you’re making meaningful comparisons, because without this, your work may become meaningless.
This book does not intend to cover statistical data analysis, since many excellent resources already address this expansive field of study.23 Instead, this chapter offers several common-sense strategies to make meaningful comparisons while you analyze your data, in order to help you design true and insightful visualizations that tell your story. You will learn to precisely choose words when describing comparisons, why and how to normalize your data, and advice on watching out for biased comparisons.
Edward R Tufte, Envisioning Information (Cheshire, CT: Graphics Press, 1990), https://www.google.com/books/edition/Envisioning_Information/_EZiAAAAMAAJ, p. 67↩︎
For a reader-friendly introduction to statistical logic and its limits, see Charles Wheelan, Naked Statistics: Stripping the Dread from the Data (W. W. Norton & Company, 2013), https://www.google.com/books/edition/Naked_Statistics_Stripping_the_Dread_fro/j5qYPqsBJb0C; David Spiegelhalter, The Art of Statistics: How to Learn from Data (Basic Books, 2019), https://www.google.com/books/edition/The_Art_of_Statistics/04-FDwAAQBAJ.↩︎