Get a rabbit: Don’t trust the numbers · 

Article by John Lanchester: “At a dinner​ with the American ambassador in 2007, Li Keqiang, future premier of China, said that when he wanted to know what was happening to the country’s economy, he looked at the numbers for electricity use, rail cargo and bank lending. There was no point using the official GDP statistics, Li said, because they are ‘man-made’. That remark, which we know about thanks to WikiLeaks, is fascinating for two reasons. First, because it shows a sly, subtle, worldly humour – a rare glimpse of the sort of thing Chinese Communist Party leaders say in private. Second, because it’s true. A whole strand in contemporary thinking about the production of knowledge is summed up there: data and statistics, all of them, are man-made.

They are also central to modern politics and governance, and the ways we talk about them. That in itself represents a shift. Discussions that were once about values and beliefs – about what a society wants to see when it looks at itself in the mirror – have increasingly turned to arguments about numbers, data, statistics. It is a quirk of history that the politician who introduced this style of debate wasn’t Harold Wilson, the only prime minister to have had extensive training in statistics, but Margaret Thatcher, who thought in terms of values but argued in terms of numbers. Even debates that are ultimately about national identity, such as the referendums about Scottish independence and EU membership, now turn on numbers.

Given the ubiquity of this style of argument, we are nowhere near as attentive to its misuses as we should be. As the House of Commons Treasury Committee said dryly in a 2016 report on the economic debate about EU membership, ‘many of these claims sound factual because they use numbers.’ The best short book about the use and misuse of statistics is Darrell Huff’s How to Lie with Statistics, first published in 1954, a devil’s-advocate guide to the multiple ways in which numbers are misused in advertising, commerce and politics. (Single best tip: ‘up to’ is always a fib. It means somebody did a range of tests and has artfully chosen the most flattering number.) For all its virtues, though, even Huff’s book doesn’t encompass the full range of possibilities for statistical deception. In politics, the numbers in question aren’t just man-made but are often contentious, tendentious or outright fake.

Two fake numbers have been decisively influential in British politics over the baleful last thirteen years. The first was an outright lie: Vote Leave’s assertion that £350 million a week extra ‘for the NHS’ would be available if we left the EU. The real number for the UK’s net contribution to the EU was £110 million, but that didn’t matter, since the crucial thing for the Leave campaign was to make the number the focus of debate. The Treasury Committee said the number was fake, and so did the UK Statistics Authority. This had no, or perhaps even a negative, effect. In politics it doesn’t really matter what the numbers are, so much as whose they are. If people are arguing about your numbers, you’re winning…(More)“.

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