Stats Chat

• The Data Journalism Awards from the Global Editors Network (scroll down past the ceremony stuff to get the links to the award-winners). And a commentary from Simon Rogers

• Colorado legalised cannabis recently. They have data on teenage cannabis use over time. It hasn’t gone up, probably because teens who wanted pot could already get it.
  I still haven’t seen anything on the two more important potential health impacts: do car crashes go up or down, does alcohol consumption go up or down.

• A nice detailed explanation of how YouGov is doing its predictions for the Brexit referendum.  At the moment they are robustly unsure, estimating 48%-53% vote for “Leave”.  They do discuss how badly the last election polling went, and conclude  “if we have a problem, it will probably not be the same problem as last time.” (via Andrew Gelman)

From the Guardian

The NHS is increasingly publishing statistics about the surgery it undertakes, following on from a movement kickstarted by the Bristol Inquiry in the late 1990s into deaths of children after heart surgery. Ever more health data is being collected, and more transparent and open sharing of hospital summary data and outcomes has the power to transform the quality of NHS services further, even beyond the great improvements that have already been made.

The problem is that most people don’t have the expertise to analyse the hospital outcome data, and that there are some easy mistakes to make (just as with school outcome data).

A group of statisticians and psychologists developed a website that tries to help, for the data on childhood heart surgery.  Comparisons between hospitals in survival rate are very tempting (and newsworthy) here, but misleading: there are many reasons children might need heart surgery, and the risk is not the same for all of them.

There are two, equally important, components to the new site. Underneath, invisible to the user, is a statistical model that predicts the surgery result for an average hospital, and the uncertainty around the prediction. On top is the display and explanation, helping the user to understand what the data are saying: is the survival rate at this hospital higher (or lower) than would be expected based on how difficult their operations are?

Team Ratings for Round 16

The basic method is described on my Department home page.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 111 matches played, 65 of which were correctly predicted, a success rate of 58.6%.
Here are the predictions for last week’s games.

Predictions for Round 16

Here are the predictions for Round 16. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday June 24 2016.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of June 18 – 24 2016 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

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Two more news sites have fallen for the cheese addiction story.

A recap for those who missed the earlier episodes:

• There was a paper using the Yale Food Addiction Scale that evaluated a lot of foods for (alleged) addictiveness.
• Pizza came top.
• Someone (we don’t know who) pushed a story to various media sites saying the research had found cheese-based foods were the most addictive (false), and that this was because of milk protein fragments called casomorphins (which aren’t even mentioned in the research paper, as you can check for yourself, and which haven’t been shown to be addictive even in mice).
• The people behind the research have disclaimed these weird interpretations of what they found. Here’s a detailed story

In a Herald (Daily Telegraph) story this week

When confronted, he confessed he’d been having an affair with a single mother he met at the school gates.

“She was vulnerable,” says Janet. “I guess he liked that. It made him feel like a hero.”

Her experience sadly chimes with the findings of a new study of more than 2,750 young married people by the University of Connecticut, which showed that men who are financially dependent on their spouses are the most likely to be unfaithful. In fact, the bigger the earning gap, the more likely they are to have an affair, with those who rely solely on their wives for their income the biggest cheats.

It turns out there are things wrong with this story, even restricting to statistical issues. To find them, you’d need to look at the research paper, which you probably can’t, because it’s paywalled.  I should note that these aren’t necessarily all criticisms of the paper for the question it was answering (which was about motivations for fidelity/infidelity). but they are for how it has been used — to turn a couple of anecdotes into a Deeply Worrying Social Trend.

First, the income data. The researcher writes

I calculated the measure from respondents’ and their spouse’s total income earned during the previous year from wages, salaries, commissions, and tips. I excluded self-employment income, because the division between labor income and business income is often measured with substantial error.

That means the group described as ‘rely solely on their wives for income‘ includes all the self-employed men, no matter how much they earn. There may well be more of them than voluntarily unemployed house-husbands.

Second, a somewhat technical point, which I think I last covered in 2012, with two posts on a story about mid-life crisis in chimpanzees.

Here’s a summary of the model given in the research paper

Notice how the curved line for men bends away from the straight line for women on both sides? And that the deviation from a straight line looks pretty symmetric? That’s forced by the statistical model.

Conceptually, the left and right sides of this graph show quite different phenomena. The right side says that the decrease in infidelity with higher relative income flattens out in men, but not in women. The left side says that the increase with lower relative income accelerates in men. The model forces these to be the same, and uses the same data to estimate both.

Since there are more men with positive than negative relative income, most of the actual evidence for men is on the right-hand side, but the newspaper story is looking at the left-hand side.

A Herald story, under the headline Selfies can damage your health

Dermatologists now believe that regularly exposing the face to the light and electromagnetic radiation from smartphones can speed up ageing and wrinkles.

The people making that claim don’t seem to be giving any evidence. You’d need a lot of evidence to believe a claim like that, given how much more light and electromagnetic radiation we get from the sun. Sure, sunlight doesn’t have quite the same frequency spectrum, but it’s so much brighter that it will be giving you more exposure at any frequency.

Also, when you’re taking selfies, your phone is usually not particularly close to your face.  After all, that’s why selfie sticks were invented. If the claim were true, the real message would be that selfies protect your health.

Doctors even claim they can tell which hand a person holds their phone in just by looking at which side of the face is most damaged.

I have no real doubt that this doctor can tell which hands his patients hold their phones in — for example, by observing what hand they use for other things, or what pocket the phone is kept in.  But, again, people nowadays don’t spend much time holding their phones up to one side of their faces. You only do that for phone calls, and only then if you don’t have a headset, and that’s such a twentieth century way to use a phone.

And, yes, the people involved are all selling something.

(This story comes from the Daily Telegraph, though it didn’t say so when I first read it, and according to a credible witness it said Daily Mail earlier)

From the Herald

According to a new study by computer scientists at Columbia University and the French National Institute, 59 per cent of links shared on social media have never actually been clicked: In other words, most people appear to retweet news without ever reading it.

Perhaps appropriately, the Herald passed on this story from the Washington Post without passing on their link to the research.

If you were reading carefully, you might notice the last part of the paragraph doesn’t actually fit the rest: it isn’t that ‘most people‘ retweet news without ever reading it, it’s that most retweets are done without reading (or before reading).  Even with that caveat, the research says that headlines matter — and I’m going to keep complaining about them.

The main focus of the research was something different, though. They were comparing clicks on the ‘primary’ URLs from the media site itself with the clicks on shortened URLs produced by readers.  Although the primary URLs had the majority of ‘impressions’, the secondary URLs added up to more clicks.  That is, when news stories are actually read via Twitter, more often than not it’s because of a personal recommendation by someone else who has actually read the story.

• Randomized controlled trial of police foot patrols, near Cambridge.

• How the Financial Times does data visualisation

• An interview with economist Claudia Goldin on the mechanisms behind the gender pay gap

• Harkanwal Singh from the Herald, talking at the NZ Open Data showcase

• The impact of open data on cities.

• The six-year war for NZ weather data (Philip Duncan isn’t unbiased on this, but he knows what he’s talking about)

So, there’s a new IARC cancer monograph out, but this one is disappointingly un-scary. (Here’s the Q&A)

There are two basic points. First, coffee looks safe. Officially it’s “inadequate evidence”, but that basically means “if we were in the business of calling things ‘safe’, coffee would be on the list”.  There has been enormous effort over decades to find health risks of coffee for all sorts of diseases, with extremely limited success. Coffee can make it harder to fall asleep, and that’s about it.

The second point is about ‘very hot drinks’. These are listed as ‘probably carcinogenic’, meaning there’s some evidence that regular consumption increases the risk of oesophageal cancer  (not that you’ll probably get cancer if you drink them).  By ‘very hot’, they mean very hot,  over 65°C when consumed, which is relatively unusual in New Zealand.  Here, it’s alcohol and smoking that are the main risks for oesophageal cancer.