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How politicians twist statistics

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How politicians twist statistics.

I scored my first 'A' at university in Statistics, a subject that at the time was distinctly unglamorous compared to the likes of Accounting, Finance and Economics.

But as one of our universities tells prospective students today: "We are increasingly becoming a data-driven society with the accumulation of massive data in many fields. Statistics is the profession associated with making meaningful sense of data; there are few disciplines that do not use statistics in some form."

Unfortunately one field that is a big user (and abuser) of statistics is politics.

Every election campaign around the world sees the electorate bombarded with statistics and numerical facts designed to persuade voters and win arguments. The problem is that quoted statistics are often contradictory.

Statistics were never meant to be confusing; quite the contrary.

You've probably heard the saying — there are three kinds of lies: lies, damned lies and statistics. In fact statistics do not lie, but the people using them often do. And because few of us have the time, patience or ability to read the data behind statistics, manipulators can use them to support a lie and convince us.

Last month The Guardian featured an article by the president of the British Royal Statistical Society, detailing how to spot "a dodgy statistic". His analysis was based on political claims ahead of the last British election and the Brexit vote.

The first of the "classic ways in which politicians meddle with statistics" is to use a proper number but change its meaning. Brexit campaigners claimed "We send the EU £350m a week". While the £350m was eye-catching, the real number (adjusted for Britain's rebate, subsidies and grants) was closer to £136m.

Another trick is the make the number look big, but not too big. Apparently once numbers get really large, say above 10 million, they all start to look the same and therefore lose their impact. If the £350m a week was replaced by the equivalent £19bn a year, voters wouldn't have paid attention.

One strategy is to casually link facts as if one caused the other. Opposition MPs said "11,000 excess deaths because we do not staff our hospitals properly at weekends". However, the number of weekend deaths actually has nothing to do with weekend staffing. It just happens that more people die at the weekends.

In 2014 one MP claimed "today there are 2,500 fewer nurses in our NHS than in May 2010" and on the same day David Cameron said "there are 3,000 more nurses in the NHS under this government". Huh? Both were actually right, they just chose different definitions. The MP compared the number of people working as nurses; Cameron used the full-time equivalent number of nurses, midwives and health specialists.

Other tricks include using total numbers rather proportions; not providing any relevant context (ie. compared to what?); exaggerating the importance of a possibly illusory change (eg. quoting a 30,000 change when a statistic has a margin of error of +/- 80,000); and prematurely announcing unofficial and selective data before the official stats come out.

The trick that we may see most of in coming months is the "if all else fails, just make the numbers up".

Last November, Donald Trump tweeted "whites killed by blacks — 81%" citing the Crime Statistics Bureau — San Francisco. But guess what? The "Bureau" does not exist and the true figure is around 15%. When confronted, Trump simply said "Am I going to check every statistic?"

Politicians have poisoned statistics. It's such a shame — I used to like them.

 

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