Posts from January 2015 (38)

On Twitter, I was sent to the headline Wind supplied 98% of Scotland’s household Power in 2014 and other Amazing Green Energy Stories.

That sounds pretty amazing. Nearly all of household electricity from wind. The next question, of course, is “And how much of the commercial and industrial electricity?”

What actually happened was that wind power supplied 40% of Scotland’s electricity last year. That’s still pretty good, but it doesn’t make such a great headline. Since household electricity was 41% of use, if you pretend that all the wind power went to households, you get 98% of household use.

The headline is misleading in the obvious way. It’s also misleading in a non-obvious way: one of the problems with wind power is that it’s generated when the wind blows, not on demand. If the power grid were set up so that household electricity was a thing, you couldn’t reasonably supply 98% of household power from wind alone.

The figure seems to come from WWF Scotland

Wind turbines provided an estimated 8,958,130MWh of electricity to the National Grid, or an average of 746,510MWh each month – enough to supply the electrical needs of 98% of Scottish households, or 2.36 million homes.

Most people who reported it didn’t go as far as the headline I quoted; on the other hand, most people didn’t pass on the data that lets you work out the real proportion.

From a new StatsNZ report on commuting in Auckland, based on the last Census.  Qualitatively, people drive to work from where you’d expect. Quantitatively, there’s been a bit of increase in public transport use.

Commuting patterns is an example of where an interactive map would be helpful for visualisation.

(via @polemic and @DarrenDavis10 on Twitter)

The Herald has a poll saying 61% of New Zealanders want to make large subsets of foreign drivers sit written and practical tests before they can drive here (33.9%: people from right-hand drive countries; 27.4% everyone but Australians). It’s hard to tell how much of this is just the push effect of being asked the questions and how much is real opinion.

The rationale is that foreign drivers are dangerous:

Overseas drivers were found at fault in 75 per cent of 538 injury crashes in which they were involved. But although failure to adjust to local conditions was blamed for seven fatal crashes, that was the suspected cause of just 26 per cent of the injury crashes.

This could do with some comparisons.  75% of 538 is 403, which is about 4.5% of all injury crashes that year.  We get about 2.7 million visitors per year, with a mean stay of 20 days (PDF), so on average the population is about 3.3% short-term visitors.

Or, we can look at the ‘factors involved’ for all the injury crashes. I get 15367  drivers of motorised vehicles involved in injury crashes, and 9192 of them have a contributing factor that is driver fault (causes 1xx to 4xx in the Crash Analysis System). This doesn’t include things like brake failures.  So, drivers on average are at fault in about 60% of the injury crashes they are involved in.

Based on this, it looks as though foreign drivers are somewhat more dangerous, but that restricting them is very unlikely to prevent more than, say, 1-2% of crashes. If you consider all the ways we might reduce injury crashes by 1-2%, and think about the side-effects of each one, I don’t think this is going to be near the top of the list.

From Stuff

Bad luck is responsible for two-thirds of adult cancer while the remaining cases are due to environmental risk factors and inherited genes, researchers from the Johns Hopkins Kimmel Cancer Center found.

The idea is that some, perhaps many, cancers come from simple copying errors in DNA replication. Although DNA copying and editing is impressively accurate, there’s about one error for every three cell divisions, even when nothing is wrong. Since the DNA error rate is basically constant, but other risk factors will be different for different cancers, it should be possible to separate them out.

For a change, this actually is important research, but it has still been oversold, for two reasons. Here’s the graph from the paper showing the ‘2/3’ figure: the correlation in this graph is about 0.8, so the proportion of variation explained is the square of that, about two-thirds.  (click to embiggen)

There are two things to notice about this graph. First, there are labels such as “Lung (smokers)” and “Lung (non-smokers)”, so it’s not as simple as ‘bad luck’.  Some risk factors have been taken into account. It’s not obvious whether this makes the correlation higher or lower.

Second, the y-axis is on a log scale, so the straight line fit isn’t to cancer incidence and the proportion of variation explained isn’t a proportion of cancer risk.  Using a log scale for incidence is absolutely right when showing the biological relationship, but you can’t read proportions of incidence explained off that graph.  This is what the graph looks like when the y-axis is incidence, either with the x-axis still on a logarithmic scale

or with neither axis on a logarithmic scale

The proportion of variation explained is 18% and 28% respectively.

It’s ok to transform the x-axis as much as we like, so I looked at a square root transformation on the x-axis (based on the slope of the log-log graph). This gets the proportion of incidence explained up to about one third. Not two-thirds.

Using the log scale gives a lot more weight to the very rare cancers in the lower left corner, which turn out not to have important modifiable risk factors. Using an untransformed y-axis gives equal weight to all cancers, which is what you want from a medical or public health point of view.

Except, even that isn’t quite right. If you look at my two graphs it’s clear that the correlation will be driven by the top three points. Two of those are familial colorectal cancers, and the incidence quoted is the incidence in people with the relevant mutations; the third is basal cell carcinoma, which barely counts as cancer from a medical or public health viewpoint

If we leave out the familial cancers and basal cell carcinoma, the proportion explained drops to about 10%.

If we leave out put back basal cell carcinoma as well, something statistically interesting happens. The correlation shoots back up again, but only because it’s being driven by a single point. A more honest correlation estimate, predicting each point based on the other points and not based on itself, is much lower.

So, in summary: the “two-thirds of cancers explained” is Just Wrong. Doing a mathematically correct calculation gives about one third. Doing a calculation that’s actually relevant to cancer in the population gives even smaller values. (update) That’s not to say that DNA replication errors are unimportant — the paper makes it clear that they are important.

We go on and on about denominators on StatsChat: the right way to report things that happen to people is usually a rate per capita rather than a total, otherwise you end up saying that Auckland has the highest number of whatever it is in New Zealand.  You do have to use the right denominator, though.

The Vatican City has the world’s highest crime rate.

That’s because the permanent population is less than 500, but the daily tourist population is about 100 times larger. The right denominator would be the tourist population.

In most countries this isn’t really an issue. For example,  in New Zealand,which has a lot of tourism, short-term visitors are only about 5% of the population. Even in the Cook Islands, residents outnumber tourists.

The Marlborough Express has a story today that begins

Kaye Nicholls tried every diet in the book without success but a fat-busting capsule produced by a Blenheim company has proved the catalyst for her weight loss.

The 54-year-old has shed a whopping 13.5 kilograms in eight weeks as part of the company’s “fat mates” trial in Blenheim.

It’s presumably no coincidence that this story appears on January 2nd, ready to exploit the New Year’s Resolution wave of dieters.

As you will have guessed, Ms Nicholls weight loss wasn’t typical. We aren’t told what the average weight loss was, just

“Tuatara Natural Products director Neil Charles-Jones said half the people on the trial lost an average of 5kg and the top 25 per cent shed more than 7kg.

That is, the average was 5kg loss among the 50% who lost the most — as far as we can tell from the story, the loss averaged over everyone could be zero.

Not only are we not told the average, the trial was uncontrolled, which makes it hard to tell how much of any benefit was due to the pill and how much just to starting a weight loss program.  The company does know that this is a problem, and so does the journalist, because the story actually says

Weight loss results were being sent to a bio-analyst to compare the capsule with the placebo effect and conclusions would be drawn by mid January.

You might wonder how they’re doing the comparison. The best way would be to look at how much weight is lost in people trying new, ineffective, weight loss products in uncontrolled trials. Slightly less good would be to use data from the placebo arm of controlled trials — it wouldn’t be as good, because we’re trying for a fair comparison, and this wasn’t a controlled trial.

However the analysis is being done, it is being done. The results will be available in a couple of weeks. If you cared about whether these pills really work, that would be the time to report the results.

If this were a medicine, controlled trials would be needed before it could be advertised and sold: the FDA criteria are weight loss of at least 5% persisting for at least a year.  It would also be illegal to use testimonials in advertising it. As it is, I’d guess a paper would think twice about accepting this story if it were a paid ad.

What’s really upsetting about the story is that this isn’t just pseudoscience. Tuatara Natural Products has public funding through both Plant & Food and Callaghan Innovation. Their product has a sensible mechanism (inhibition of α-amylase in the gut to slow down carbohydrate absorption). They should be interested in doing better.

(note: JohnPickering has a grumpier post about the same story)

The Dominion Post asked motorists why they thought the road toll had climbed, and what should be done about it.

Interestingly, three of the five(middle-aged, white, male ,Wellington area) motorists attributed it to random variation. That’s actually possible: the evidence for a real change in risk nationally is pretty modest (and the Wellington region toll is down on last year).

(via @anderschri5 on Twitter)

From the science journal Nature, who should know better than to quote big-sounding numbers without context.

That’s roughly the same number of person-hours as the world spent watching the China Central Television news program Xīnwén Liánbō: estimated at 135 million people for half an hour a day.  Or about 1/3 as much time as spent watching YouTube.