Sister blog of Physicists of the Caribbean. Shorter, more focused posts specialising in astronomy and data visualisation.

Thursday, 8 February 2018

Statistics on a postcard

Very nice article on understanding statistics, summarised on a postcard :
https://www.ft.com/content/ba4c734a-0b96-11e8-839d-41ca06376bf2

Or if you prefer, an audio interview is here :
https://www.ft.com/content/850fa787-b463-4156-a11d-da70c8620eb3

"But wait !", I hear you cry. "Don't you already have a nearly identical blog post about this ?"
"Yes," I reply, "I do. And since I'm resharing something of near-identical content to my own, I suppose this means I'm guilty of confirmation bias. Also, talking to myself."
http://astrorhysy.blogspot.cz/2015/11/sense-and-sensible-statistics.html


“It is better to be vaguely right than exactly wrong,” wrote Carveth Read in Logic (1898), and excessive precision can lead people astray. On the eve of the US presidential election in 2016, the political forecasting website FiveThirtyEight gave Donald Drumpf a 28.6 per cent chance of winning. In some ways that is impressive, because other forecasting models gave Drumpf barely any chance at all. But how could anyone justify the decimal point on such a forecast? No wonder many people missed the basic message, which was that Drumpf had a decent shot. “One in four” would have been a much more intuitive guide to the vagaries of forecasting.

Exaggerated precision has another cost: it makes numbers needlessly cumbersome to remember and to handle. So, embrace imprecision. The budget of the NHS in the UK is about £10bn a month. The national income of the United States is about $20tn a year. One can be much more precise about these things, but carrying the approximate numbers around in my head lets me judge pretty quickly when — say — a £50m spending boost or a $20bn tax cut is noteworthy, or a rounding error.

I want to encourage us all to make the effort a little more often: to be open-minded rather than defensive; to ask simple questions about what things mean, where they come from and whether they would matter if they were true. And, above all, to show enough curiosity about the world to want to know the answers to some of these questions — not to win arguments, but because the world is a fascinating place.

3 comments:

  1. Yup. The very concept of "statistical significance" is a number we pluck from the air. 1 in 20? 1 in 100?

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  2. Even 1 in a million isn't very impressive if you've got a billion data points to work with. :P

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  3. I've always been wary of big data for that reason. You should see the way we push around inversions in geophysics. "Well we reckon that's a fault so..."

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