Everydata: The Misinformation Hidden in the Little Data You Consume Every Day Front Cover

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day

  • Length: 240 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2016-04-12
  • ISBN-10: 1629561010
  • ISBN-13: 9781629561011
  • Sales Rank: #794526 (See Top 100 Books)
Description

While everyone is talking about “big data,” the truth is that understanding the “little data”—the stats that underlie newspaper headlines, stock reports, weather forecasts, and so on—is what helps you make smarter decisions at work, at home, and in every aspect of your life. The average person consumes approximately 30 gigabytes of data every single day, but has no idea how to interpret it correctly. EVERYDATA explains, through the eyes of an expert economist and statistician, how to decipher the small bytes of data we consume in a day.

EVERYDATA is filled with countless examples of people misconstruing data—with results that range from merely frustrating to catastrophic:

The space shuttle Challenger exploded in part because the engineers were reviewing a limited sample set.
Millions of women avoid caffeine during pregnancy because they interpret correlation as causation.
Attorneys faced a $1 billion jury verdict because of outlier data.
Each chapter highlights one commonly misunderstood data concept, using both realworld and hypothetical examples from a wide range of topics, including business, politics, advertising, law, engineering, retail, parenting, and more. You’ll find the answer to the question—“Now what?”—along with concrete ways you can use this information to immediately start making smarter decisions, today and every day.

Table of Contents

Chapter 1 Data, Data, Everywhere: An Introduction to Everydata
Chapter 2 The Challenger Challenge: How Sampling Can Affect Results
Chapter 3 Red State Blues: Averages and Aggregates—A Closer Look at Summary Statistics
Chapter 4 Are You Smarter Than an iPhone-Using, Radiohead-Loving Republican?: Understanding Correlation Versus Causation
Chapter 5 In Statistics We Trust: Is What You’re Seeing True?
Chapter 6 Shrinking Africa: Misrepresentation and Misinterpretation
Chapter 7 Spoonfed Data: When Cherry Picking Goes Bananas
Chapter 8 Predicting Disaster: Forecasting the Future
Chapter 9 It’s a Jungle Out There: Putting It All Together

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