Professor Sir David Spiegelhalter
speaker
351 appearances
17 recordings
1 series
first heard Oct 2010
last heard Jan 2021
Professor Sir David Spiegelhalter’s voice in public audio — every appearance, attributed to the second.
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Appearances
They're both going to die in the end.
but one's got this extra annual risk of dying.
The person who eats more meat is expected to live one year less than the person who doesn't eat so much meat.
The 40-year-old who does eat the meat, you'd expect on average him to live to 79, and the person who doesn't eat so much meat, you expect him to live till 80.
That one year off our life between age 40 and 80, that's equivalent to losing half an hour a day.
So that means that the meat eater, on average, when he's sitting eating his burger, is losing half an hour of life because of that meal, on average.
It's equivalent, you know, scaled up over a lifetime of smoking two cigarettes, for example, is about half an hour of your life.
Or being a bit overweight, five kilograms overweight, which I am, I'm five kilograms overweight.
I think you need to distinguish between stories that crop up all the time and basically say the same thing every time, which is what stories like this tend to, which must be a bit monotonous to hear, but it's not bad to be reminded about things that do seem to be the case.
One of the problems is, of course, that what tends to get in the news is when a story comes up that's different from what people have heard before.
Now that, I think, is really dangerous because people say, oh, well, scientists say one thing and then they say another, they can't make up their minds, why should I believe anything?
And yet, actually, it's just one study.
And if one study goes against the mass of evidence that's previously been published, there's good reason to doubt that study.
But yet, that's the very study that gets all the publicity from the media.
Well, I must say, first of all, this is quite tricky stuff and it's very easy to get it wrong.
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I think a Bayes theorem is a formal way of using imperfect evidence to weigh up competing explanations.
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And those competing explanations might be, in a legal case, whether someone's guilty or innocent.
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And this is based on what's called the likelihood ratio, which is a fairly straightforward thing.
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It's the relative likelihood of the evidence under the different competing explanations.
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How likely is it?
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Showing 221–240 of 351 · page 12 of 18
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