Another book down on my to-read list; The Tiger That Isn’t: Seeing through a world of numbers by Michael Blastland and Andrew Dilnot. The title is quite descriptive – this is a book teaching normal people to understand numbers, especially those often ill-presented and trusted by the media and/or politicians. One can hardly look through a single page in a newspaper these days without seeing a barrage of numbers being quoted, whether it’s on the stock market or a latest crime statistic or something else. So it’s obviously an important thing to understand the numbers to see if a) they’re telling the truth and b) whether the attached story agrees with the quoted numbers?
The book starts from the very basics; for example, whenever you encounter a figure such as, say, €100M, it’s very important to ask one simple question: Is it a big number? A deficit of €100M can seem absolutely huge when quoted in a headline, but it can in fact be inconsequentially small depending on the case. Other critically important topics include chapters on the importance of chance, how numbers just move up and down naturally and many others. Statistical caveats such as sampling and probabilities and percentiles are thoroughly discussed.
The book is filled with frankly appalling examples on how governments trust and act on figures that don’t even remotely represent the truth, how people are absolutely terrible in determining the scale of some things and so on. It also shows how horribly inaccurate – at best – most data is, but remembers to remind us in detail of the difficulties in gathering quality data. And also, importantly, stresses that just because getting good numbers is difficult doesn’t mean we should throw our hands up in the air and give up on all numbers altogether – they do have immense power if done right.
It is easy to concur with New Scientist’s comment on The Tiger That Isn’t, namely: “Every journalist should get paid leave to read and reread The Tiger That Isn’t until they’ve understood how they are being spun” – and the same actually goes for politicians and everyone who wishes to understand how we are daily being misled.
On a negative side, the writing was sometimes even too spelled-out and simple, leading to sometimes slow progress on the actual topic. Also, by far the most examples were from the UK; I would’ve preferred a more international approach. Fortunately, similar examples than what are quoted are available from most other countries, with equally appalling handling of the figures. Another aspect that could be seen as negative is that the writers tend to – though rightly so – downplay the likelihood of rare possibilities whereas the media often emphasizes the extreme possibilities. Unfortunately, as was the case with house price collapse in the US and UK, the extreme forecasts sometimes are the accurate ones. While it does nobody any good to focus solely on the most unlikely events, the possibility of black swans shouldn’t be dismissed altogether either.
Overall, I would classify the book as a very good (I would say) introduction to the importance of data and properly made statistics, a good teaching aid to detect numbers that aren’t up to snuff as well as a stern warning against relying on numbers that could’ve just as well been made up. Asking a few relatively simple background questions when you’re faced with a number will give you a much better understanding on its importance. If you already don’t do this, reading this book will make you look at statistics with a new sense of interest, skepticism and, hopefully, strive to understand them better and get to the bottom of things.
