Introduction --
How numbers can mislead --
Anecdotal evidence --
Sample sizes --
Biased samples --
Statistical significance --
Effect size --
Confounders --
Causality --
Is that a big number? --
Bayes’ theorem --
Absolute vs relative risk --
Has what we’re measuring changed? --
Rankings --
Is it representative of the literature? --
Demand for novelty --
Cherry-picking --
Forecasting --
Assumptions in models --
Texas sharpshooter fallacy --
Survivorship bias --
Collider bias --
Goodhart’s law --
Conclusion and statistical style guide.

“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’s description.