This book explains the many ways that people can misuse statistics to mislead you. Maybe a more descriptive title would be, “How to Tell When People are Lying to You with Statistics”. It’s a bit like being shown how a magician does a trick – except that at least with the magic trick, you already know you’re being fooled.
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Google remembers every search anyone does. If you combine all that data in the right ways, you can come up with a lot of results, theories and conclusions. That’s what Seth Stephens-Davidowitz does in this book. The title comes from one of his conclusions: people say they do things (in surveys and so on) but the aggregated search data from Google shows that a large number of them are lying. It’s a clever idea and he draws out a lot of interesting material (enough to fill a book!). If you’ve ever typed “why does my cat” into Google just to see what suggestions pop up, then this book is for you.
This is a great survey of all the ways to lie with statistics, and how to avoid being fooled by them. So many of the things we read and hear are based on numerical data, and often it’s hard to argue with them — “the numbers don’t lie”, they say. And it’s true: numbers don’t lie. But people lie, sometimes using words and sometimes using numbers.
There are sections on politics, discussing gerrymandering and also counting election results. Seife’s analysis of the 2000 US presidential election is excellent, laying bare the frankly ridiculous voting systems in use. He also reveals what the actual result should have been, after all the court cases and recounts. His conclusion surprised me, but it is actually the only sensible option even though it would probably have caused outrage.
1. Yes, low voter turnout favours the right-wing parties.
2. If everyone had voted, the result would have been much closer: the Labour Party may have been able to form a government. Continue reading
Back in the 1980s four all-rounders dominated the world test cricket scene: Ian Botham from England, Kapil Dev from India, Imran Khan (now a very prominent politician) from Pakistan, and Richard Hadlee from New Zealand. Much ink was spilled in the debate on who was the best, and how they compared with great all-rounders from the past such as Australia’s Keith Miller and Garfield Sobers from the West Indies. Many years ago I came up with a good way of evaluating all-rounders based on their statistics, and finally I have been able to crunch the numbers and come up with the results.
I should clarify a few things. This whole article relates only to men’s test cricket, though it would also apply to any other format. More importantly, the whole idea of rating players based on statistics is obviously flawed, as stats don’t capture everything about a player. But as long as we allow for that and don’t try to be too precise, I think we can gain some useful and interesting insights.