In a world of fake news and misinformation, the Chivers’ brilliantly clear and accessible guide to understanding the vagaries, ambiguities and plain untruths of statistics provides a sane and logical way to pick through the minefield of contemporary current affairs.
'Even one glass of wine a day raises the risk of cancer'
'Hate crimes have doubled in five years'
'Fizzy drinks make teenagers violent'
Every day, most of us will read or watch something in the news that is based on statistics in some way. Sometimes it'll be obvious - 'X people develop cancer every year' - and sometimes less obvious - 'How smartphones destroyed a generation'. Statistics are an immensely powerful tool for understanding the world; the best tool we have. But in the wrong hands, they can be dangerous.
This book will help you spot common mistakes and tricks that can mislead you into thinking that small numbers are big, or unimportant changes are important. It will show you how the numbers you read are made - you'll learn about how surveys with small or biased samples can generate wrong answers, and why ice cream doesn't cause drownings.
We are surrounded by numbers and data, and it has never been more important to separate the good from the bad, the true from the false. How To Read Numbers is a vital guide that will help you understand when and how to trust the numbers in the news - and, just as importantly, when not to.
Publisher: Orion Publishing Co
Number of pages: 208
Weight: 328 g
Dimensions: 222 x 142 x 24 mm
'A charming, practical and insightful guide. You might not even notice how much you're learning - you'll be too busy having fun' - Tim Harford, author of How to Make the World Add Up
'A vital plea to take statistics more seriously - the prose being as clear and elegant as the numbers' - Sathnam Sanghera
'Reading this book is strongly correlated with not looking stupid. Highly recommended' - Helen Lewis, author of Difficult Women
'An erudite, enlightening guide to the numbers we read in the news - and why they are so often wrong. The authors make sense of dense material and offer engrossing insights into sampling bias, statistical significance and the dangers of believing the casual language used in newspapers' - Independent
'One of the best science writers in the business' - TLS
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