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Something Doesn’t Add Up: Surviving Statistics in a Post-Truth World
Some people fear and mistrust numbers. Others want to use them for everything. After a long career as a statistician, Paul Goodwin has learned the hard way that the ones who want to use them for everything are a very good reason for the rest of us to fear and mistrust them. Something Doesn't Add Up is a fieldguide to the numbers that rule our world, even though they don't make sense.
Wry, witty and humane, Goodwin explains mathematical subtleties so painlessly that you hardly need to think about numbers at all. He demonstrates how statistics that are meant to make life simpler often make it simpler than it actually is, but also reveals some of the ways we really can use maths to make better decisions. Enter the world of fitness tracking, the history of IQ testing, China's social credit system, Effective Altruism, and learn how someone should have noticed that Harold Shipman was killing his patients years before they actually did. In the right hands, maths is a useful tool. It's just a pity there are so many of the wrong hands about.
Wry, witty and humane, Goodwin explains mathematical subtleties so painlessly that you hardly need to think about numbers at all. He demonstrates how statistics that are meant to make life simpler often make it simpler than it actually is, but also reveals some of the ways we really can use maths to make better decisions. Enter the world of fitness tracking, the history of IQ testing, China's social credit system, Effective Altruism, and learn how someone should have noticed that Harold Shipman was killing his patients years before they actually did. In the right hands, maths is a useful tool. It's just a pity there are so many of the wrong hands about.
- GenresNonfictionScience
256 pages, Hardcover
First published February 27, 2020
15 people are currently reading
172 people want to read
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Displaying 1 - 12 of 12 reviews
February 28, 2020
If there's one thing that's better than a juicy statistic, it's enjoying the process of pulling apart a dodgy one. It's why the radio programme More or Less is so excellent - so Paul Goodwin's book, subtitled 'surviving statistics in a post-truth world' was something I was really looking forward to - but for reasons I find it hard to put my finger on, it doesn't quite hit the spot.
Goodwin, a maths professor at the University of Bath, starts with a series of chapters telling us what's wrong with many of the statistics we see everyday. And he makes good points. We discover the dangers of rankings and trying to summarise a complex distinction in a single measure. We see why proxies are poor (essentially, if you can't actually measure what you want to, using something else that might be an appropriate indicator, but often isn't). We explore why polls are problematic. And there's a bit on Bayesian statistics and how it still tends to be disregarded by some, including the courts.
This is almost all negative, which is fine. Books like The Tiger that Isn't, one of my favourite titles on dodgy numbers and statistics take just such an approach. But they do so with lots of interesting stories and a plethora of examples. Although Goodwin does use some specifics, they feel more like case studies - they just don't engage in the way they should and there are too many generalities.
The other side of the book is we're promised a toolkit to help us cut through dodgy statistics. This is a good idea, but I'm really not sure how to use much of it in practice. For example, one instruction is 'If a questionnaire was used to obtain the number, was it biased?' With specifics such as checking whether, for example, it's based on leading questions, or questions which unrealistically limit people's response options. But I don't see how this can be used. This is supposed to be a toolkit to help ordinary people deal with statistics in the media (social and mainstream) - but how often does an article include details of the questionnaire used, or even the sample size? How are we supposed to answer these questions?
One last observation - the author proved at one point to be, perhaps surprisingly, honest. He tells us of an experiment he did showing students information on different tech products, asking which they would prefer, then repeating the exercise twice over four months, finding their choices were not set in stone, but changed. Goodwin points out limitations: that there may have been changes in technology over that period, news and reviews could have changed opinions, or as they weren't actually buying the technology, the students might not have cared much about the choice. 'But some of the inconsistency may have arisen simply because the respondents didn't really know what their true preferences were.' This is true, but equally it may not - in effect, he's telling us it wasn't a very useful study. (It would be interesting to ask, for example, why products that stay the same for decades and aren't likely to be reviewed, such as chocolate bars, weren't used, rather than tech?) Admitting this is quite brave.
I didn't dislike the book, and although it inevitably wheels out a lot of familiar examples, there were some new ones I hadn't come across before. But there was something about the presentation that just didn't do it for me. Even so, if, like me, you collect titles on dodgy statistics and how to deal with them, it's definitely one to add to the collection.
Goodwin, a maths professor at the University of Bath, starts with a series of chapters telling us what's wrong with many of the statistics we see everyday. And he makes good points. We discover the dangers of rankings and trying to summarise a complex distinction in a single measure. We see why proxies are poor (essentially, if you can't actually measure what you want to, using something else that might be an appropriate indicator, but often isn't). We explore why polls are problematic. And there's a bit on Bayesian statistics and how it still tends to be disregarded by some, including the courts.
This is almost all negative, which is fine. Books like The Tiger that Isn't, one of my favourite titles on dodgy numbers and statistics take just such an approach. But they do so with lots of interesting stories and a plethora of examples. Although Goodwin does use some specifics, they feel more like case studies - they just don't engage in the way they should and there are too many generalities.
The other side of the book is we're promised a toolkit to help us cut through dodgy statistics. This is a good idea, but I'm really not sure how to use much of it in practice. For example, one instruction is 'If a questionnaire was used to obtain the number, was it biased?' With specifics such as checking whether, for example, it's based on leading questions, or questions which unrealistically limit people's response options. But I don't see how this can be used. This is supposed to be a toolkit to help ordinary people deal with statistics in the media (social and mainstream) - but how often does an article include details of the questionnaire used, or even the sample size? How are we supposed to answer these questions?
One last observation - the author proved at one point to be, perhaps surprisingly, honest. He tells us of an experiment he did showing students information on different tech products, asking which they would prefer, then repeating the exercise twice over four months, finding their choices were not set in stone, but changed. Goodwin points out limitations: that there may have been changes in technology over that period, news and reviews could have changed opinions, or as they weren't actually buying the technology, the students might not have cared much about the choice. 'But some of the inconsistency may have arisen simply because the respondents didn't really know what their true preferences were.' This is true, but equally it may not - in effect, he's telling us it wasn't a very useful study. (It would be interesting to ask, for example, why products that stay the same for decades and aren't likely to be reviewed, such as chocolate bars, weren't used, rather than tech?) Admitting this is quite brave.
I didn't dislike the book, and although it inevitably wheels out a lot of familiar examples, there were some new ones I hadn't come across before. But there was something about the presentation that just didn't do it for me. Even so, if, like me, you collect titles on dodgy statistics and how to deal with them, it's definitely one to add to the collection.
November 12, 2020
Brilliant! Gives a good overview of how numbers can be easily manipulated while also being presented in a reader-friendly format. Really opens your eyes to how misleading headlines can be. Recommended for anyone who would like some light reading while also learning something useful.
March 24, 2021
Data as a means or data as an end. This book focusses on this aspect from different perspectives. It does not berate data rather sends out a caution over over-reliance on data. Sometimes the writing does get a little monotonous. Overall though, it is a good one-time read book.
June 21, 2025
I liked the title. To be fair, not the full content was worth only one star. Typically each chapter started with an interesting notion or story, followed by several pages of seemingly unrelated stories, words, ideas, leaving me as a reader wondering in which direction the author was heading. What is his take home message?
I think adding all of those up, the book would have been considerably smaller and better to read.
Indeed, something doesn't add up, mostly how you can write a compelling and logical book.
I think adding all of those up, the book would have been considerably smaller and better to read.
Indeed, something doesn't add up, mostly how you can write a compelling and logical book.
April 25, 2021
Insightful, amusing and mostly easy to read.
While the famous How to Lie with Statistics talks about figures that deliberately deceive, Goodwin tells us where journalists and pollsters in good faith collect numbers that make no sense at all.
Read a longer review on my blog: https://nouw.com/cwidmann/something-d...
While the famous How to Lie with Statistics talks about figures that deliberately deceive, Goodwin tells us where journalists and pollsters in good faith collect numbers that make no sense at all.
Read a longer review on my blog: https://nouw.com/cwidmann/something-d...
June 7, 2020
This is an excellent book to understand numbers around us for non-statisticians. It is well-written, with plenty of exciting and vivid examples, jokes and stories from life.
Highly recommend if you don’t like statistics, but you are curious about our current data-driven world and all this numeric ‘facts’ that surround us every day.
Highly recommend if you don’t like statistics, but you are curious about our current data-driven world and all this numeric ‘facts’ that surround us every day.
November 15, 2022
This book might help enhance people's common sense of numbers. It's a delightful reading but only a few new things to learn.
By the way, most of the takeaways are concentrated in the appendix...So if you are at the bookstore...Well, have a good day.
By the way, most of the takeaways are concentrated in the appendix...So if you are at the bookstore...Well, have a good day.
April 30, 2022
5 stars. Brilliant book! Full review to follow.
May 3, 2022
Nothing here to blow my mind, but certainly an enjoyable read.
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August 15, 2024p value 95% confidence level was randomly selected and can't wait to visit the over-the-budget 1976 Montreal Olympics site soon
people remember what happened between age 10-30 the most
people remember what happened between age 10-30 the most
December 29, 2024
Compact insightful book on how we should all exercise caution when viewing numbers on variety of medias from magazines to journals to book prize lists etc.
Highly highly recommend
Highly highly recommend
August 6, 2024
It’s so-so for the similar books.
Displaying 1 - 12 of 12 reviews