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Descrição do livro
Reading Data Visualizations turns data visualization literacy into an inspectable subject by naming the parts that affect judgment. The central misunderstanding is usually not lack of information, but confidence built from too few parts. The book develops a working map of the field before asking readers to compare claims or choose a course of action. The risk chapters ask what happens when dashboards, chart junk, or misleading graphs is misunderstood, ignored, or overstated. Examples are chosen to expose both useful patterns and the boundaries of what a general reader can infer about sampling. The finished understanding is practical: clearer terms, better comparisons, and more honest limits. The final chapters synthesize terminology, mechanism, risk, and application into a usable decision lens. The examples preserve the limits of general knowledge while giving readers better questions for specialists. The book also uses axis scales scenarios to separate durable principles from advice that only works in narrow cases. The reader sees how confidence intervals can support a decision in one setting and mislead in another.
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