'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.
Author(s): Tom Chivers, David Chivers
Publisher: The Orion Publishing Group Ltd
Year: 2021
Language: English
Pages: 102
City: London
Tags: Statistics, Data Analysis, Infographics
Dedication
Title Page
Contents
Introduction
Chapter 1: How Numbers Can Mislead
Chapter 2: Anecdotal Evidence
Chapter 3: Sample Sizes
Chapter 4: Biased Samples
Chapter 5: Statistical Significance
Chapter 6: Effect Size
Chapter 7: Confounders
Chapter 8: Causality
Chapter 9: Is That a Big Number?
Chapter 10: Bayes’ Theorem
Chapter 11: Absolute vs Relative Risk
Chapter 12: Has What We’re Measuring Changed?
Chapter 13: Rankings
Chapter 14: Is It Representative of the Literature?
Chapter 15: Demand for Novelty
Chapter 16: Cherry-picking
Chapter 17: Forecasting
Chapter 18: Assumptions in Models
Chapter 19: Texas Sharpshooter Fallacy
Chapter 20: Survivorship Bias
Chapter 21: Collider Bias
Chapter 22: Goodhart’s Law
Conclusion and Statistical Style Guide
Acknowledgements
Notes
Also by Tom Chivers
Copyright