The Numbers Game
The Commonsense Guide to Understanding Numbers in the News,in Politics, and in Life
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- USD 4.99
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- USD 4.99
Descripción editorial
The Strunk and White of statistics team up to help the average person navigate the numbers in the news
Drawing on their hugely popular BBC Radio 4 show More or Less, journalist Michael Blastland and internationally known economist Andrew Dilnot delight, amuse, and convert American mathphobes by showing how our everyday experiences make sense of numbers.
The radical premise of The Numbers Game is to show how much we already know and give practical ways to use our knowledge to become cannier consumers of the media. If you've ever wondered what "average" really means, whether the scare stories about cancer risk should convince you to change your behavior, or whether a story you read in the paper is biased (and how), you need this book. Blastland and Dilnot show how to survive and thrive on the torrent of numbers that pours through everyday life.
PUBLISHERS WEEKLY
Americans are assaulted by numbers, whether it's the latest political poll or most recent clinical study on caffeine. But what do these numbers really mean and are they communicating a categorical truth? Blastland and Dilnot, from the BBC radio show More or Less, embark on a monumental task of interpreting numerical data and showing how its misinterpretation often leads to misinformation. "It is one thing to measure," they write, "quite another to wrench the numbers to a false conclusion." The authors take a close look at statistics that are accepted at face value many stemming from scientific or medical discoveries. They examine everything from the link between alcohol and breast cancer risk to baseball batting averages to fascinating assessments of the manipulation of data by politicians when they talk taxes or the cautionary tale of a U.K. educational measurement program designed much like No Child Left Behind. Blastland and Dilnot apply their famously cheeky approach to the analysis of how people are duped, frightened or falsely encouraged by data.
