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Stian Westlake, chief executive of the Royal Statistical Society, in October 2020 said:
From remote working to online shopping, Covid-19 has been a great accelerator of pre-existing trends. One phenomenon it has supercharged is our love-hate relationship with statistics, data and algorithms. The threat of the pandemic has made us crave data: millions pore over R numbers; the technicalities of vaccine trials and testing accuracy, once of interest only to biostatisticians, are front-page news.
But Covid-19 has also cranked up our latent data-phobia. Can we trust the statistics our governments are publishing about the virus? Might track-and-trace apps unacceptably compromise our privacy? Covid even gave the UK its first algorithmic political scandal, as the grades of school leavers unable to sit exams due to lockdown were downgraded by what the prime minister described as a “mutant algorithm”.
In How to Make the World Add Up, Tim Harford, the FT’s Undercover Economist, offers us 10 rules for how to think effectively about numbers and data in a world where statistics matter more than ever. His tips are refreshingly human, and grounded as much in common sense as quantitative wizardry.
Rule One is “search your feelings” — because our emotional biases often get in the way of looking sensibly at situations, even if our maths are flawless. Rule Seven advises us to demand openness when dealing with algorithms, without which their limitations can often be hidden. Such advice would certainly have helped avoid the UK’s exam grading fiasco, which, as the Royal Statistical Society observed at the time, was less a case of a mutant algorithm and more to do with a lack of transparency.
Statistical integrity has an important role to play in the book, with an eloquent plea for governments to invest in honest statistics. This feels particularly urgent at a time when the US government is attempting to intervene in the running of the national census for transparently political motives.
But Harford is at pains to point out that responsible statistical practice is about more than keeping the dodgy numbers out. He explicitly contrasts his book with Darrell Huff’s classic How to Lie with Statistics — said to be the best-selling statistics book ever, published in 1954 and still in print. For Huff, statistics were a cute trick, mainly useful for politicians and advertisers to pull the wool over people’s eyes. Harford takes a more positive view: the right statistics can be a force for good, and we should delight in their usefulness.
The rules are wise and useful, and should be taken to heart by anyone who deals with numbers and data. But what makes this book such a delight are two other, more unexpected qualities.
Our emotional biases often get in the way of looking sensibly at situations, even if our maths are flawless
The first is Harford’s entertaining sense of mischief, which lends a twist to several of the stories in the book. We learn that Huff cashed in on his publishing fame with an inglorious second career as a tobacco lobbyist, turning his cool scepticism into a weapon to try to discredit the well-evidenced case that smoking causes cancer. During a fascinating discussion of the replication crisis in psychological research — a scandal in which many famous findings were found to be the result of random chance, combined with questionable research design — Harford gently notes that these findings form the backbone of more than a few non-fiction blockbusters (books that might well grace the reader’s own shelves).
The book’s other distinctive virtue is a beguiling sense of curiosity that manifests itself in the range of captivating anecdotes. We learn about everything from the psychology of passing off a fake Vermeer (lesson: it’s easiest to be fooled if you want to believe the lie) to the investment strategies of Irving Fisher and John Maynard Keynes (flexibility is good, and sometimes you learn this by being wrong).
Just as important is the book’s appreciation of curiosity for its own sake — in Orson Welles’s words, “once people are interested, they can understand anything in the world”.
As such, How to Make the World Add Up brings out an important paradox of statistics as a discipline. We tend to think of statistics as a practical subject, helping us analyse problems, from how to identify effective vaccines to how to contain viral outbreaks. Like mechanical engineering or software development, it has clear worldly benefits, and obvious economic returns. But Harford’s book also highlights the conceptual importance of statistics. It offers us a way of thinking seriously about uncertainty, providing a framework for how we understand the world, and training ourselves not to be deceived.
How to Make the World Add Up: Ten Rules for Thinking Differently About Numbers, by Tim Harford, The Bridge Street Press, RRP£20, 352 pages
But Covid-19 has also cranked up our latent data-phobia. Can we trust the statistics our governments are publishing about the virus? Might track-and-trace apps unacceptably compromise our privacy? Covid even gave the UK its first algorithmic political scandal, as the grades of school leavers unable to sit exams due to lockdown were downgraded by what the prime minister described as a “mutant algorithm”.
In How to Make the World Add Up, Tim Harford, the FT’s Undercover Economist, offers us 10 rules for how to think effectively about numbers and data in a world where statistics matter more than ever. His tips are refreshingly human, and grounded as much in common sense as quantitative wizardry.
Rule One is “search your feelings” — because our emotional biases often get in the way of looking sensibly at situations, even if our maths are flawless. Rule Seven advises us to demand openness when dealing with algorithms, without which their limitations can often be hidden. Such advice would certainly have helped avoid the UK’s exam grading fiasco, which, as the Royal Statistical Society observed at the time, was less a case of a mutant algorithm and more to do with a lack of transparency.
Statistical integrity has an important role to play in the book, with an eloquent plea for governments to invest in honest statistics. This feels particularly urgent at a time when the US government is attempting to intervene in the running of the national census for transparently political motives.
But Harford is at pains to point out that responsible statistical practice is about more than keeping the dodgy numbers out. He explicitly contrasts his book with Darrell Huff’s classic How to Lie with Statistics — said to be the best-selling statistics book ever, published in 1954 and still in print. For Huff, statistics were a cute trick, mainly useful for politicians and advertisers to pull the wool over people’s eyes. Harford takes a more positive view: the right statistics can be a force for good, and we should delight in their usefulness.
The rules are wise and useful, and should be taken to heart by anyone who deals with numbers and data. But what makes this book such a delight are two other, more unexpected qualities.
Our emotional biases often get in the way of looking sensibly at situations, even if our maths are flawless
The first is Harford’s entertaining sense of mischief, which lends a twist to several of the stories in the book. We learn that Huff cashed in on his publishing fame with an inglorious second career as a tobacco lobbyist, turning his cool scepticism into a weapon to try to discredit the well-evidenced case that smoking causes cancer. During a fascinating discussion of the replication crisis in psychological research — a scandal in which many famous findings were found to be the result of random chance, combined with questionable research design — Harford gently notes that these findings form the backbone of more than a few non-fiction blockbusters (books that might well grace the reader’s own shelves).
The book’s other distinctive virtue is a beguiling sense of curiosity that manifests itself in the range of captivating anecdotes. We learn about everything from the psychology of passing off a fake Vermeer (lesson: it’s easiest to be fooled if you want to believe the lie) to the investment strategies of Irving Fisher and John Maynard Keynes (flexibility is good, and sometimes you learn this by being wrong).
Just as important is the book’s appreciation of curiosity for its own sake — in Orson Welles’s words, “once people are interested, they can understand anything in the world”.
As such, How to Make the World Add Up brings out an important paradox of statistics as a discipline. We tend to think of statistics as a practical subject, helping us analyse problems, from how to identify effective vaccines to how to contain viral outbreaks. Like mechanical engineering or software development, it has clear worldly benefits, and obvious economic returns. But Harford’s book also highlights the conceptual importance of statistics. It offers us a way of thinking seriously about uncertainty, providing a framework for how we understand the world, and training ourselves not to be deceived.
How to Make the World Add Up: Ten Rules for Thinking Differently About Numbers, by Tim Harford, The Bridge Street Press, RRP£20, 352 pages
