Stats Chat

Team Ratings for the Currie Cup Final

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 37 matches played, 27 of which were correctly predicted, a success rate of 73%.
Here are the predictions for last week’s games.

Predictions for the Currie Cup Final

Here are the predictions for the Currie Cup Final. 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.

• From the Herald last week, via Aaron Schiff, why you may regret giving your kid a cool name. From Eric Crampton, the last time this story came around, what it means

• Beautiful weather maps from Ventusky, via Jenny Bryan

• From BusinessInsider: 90% of executive board members think the ideal proportion of women on boards is higher than the current 20%, but the majority think it should still be 40% or less.

• The Ministry for Social Development is collecting more data on people who use government-support community services. On one hand, they’re less likely to misuse it than a lot of internet companies; on the other hand, it might well deter people from seeking help. And while the Ministry is getting written consent, the people obtaining it won’t get paid by the Ministry if consent isn’t given.

• If you only read one summary of the state of the US elections, the 538 update is a relatively painless and informative one.
• People might be worrying too much about hackers (techy)

Moreover, we find that cyber incidents cost firms only a 0.4% of their annual revenues, much lower than retail shrinkage (1.3%), online fraud (0.9%), and overall rates of corruption, financial misstatements, and billing fraud (5%).

Machine learning is like a deep-fat fryer. First time you try it you think “Amazing, I bet this will work on anything!” And it kind of does

— Pinboard (@Pinboard) July 7, 2016

“Kind of” being an important qualifier here.

It’s time again for the only bogus poll that StatsChat endorses: the New Zealand Bird of the Year.

Why is Bird of the Year ok?

• No-one pretends the result means anything real about popularity
• The point of the poll is just publicity for the issue of bird conservation
• Even so, it’s more work to cheat than for most bogus polls

Why takahē?

• Endangered
• Beautiful (if dumb)
• Very endangered
• Unusual even by NZ bird standards: most of their relatives (the rail family) are shy little waterbirds.

(A sora, a more-typical takahē relative, by/with ecologist Auriel ‘@RallidaeRule’ Fournier)

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 October 21 2016.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of October 15 – 21 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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From the New York Times: “How One 19-Year-Old Illinois Man Is Distorting National Polling Averages”

There is a 19-year-old black man in Illinois who has no idea of the role he is playing in this election.

He is sure he is going to vote for Donald J. Trump.

I think the story exaggerates the impact of this guy’s opinions on polling averages, but it’s a great illustration of one of the subtleties of polling.

Even in New Zealand, you often see people claiming, for example, that opinion polls will underestimate the Green Party vote because Green voters are younger and more urban, and so are less likely to have landline phones. As we see from the actual elections, that isn’t true. Pollers know about these simple forms of bias, and use weighting to fix them — if they poll half as many young voters as they should, each of their votes counts twice. Weighting isn’t as good as actually having a representative sample, but it’s ok — and unlike actually having a representative sample, it’s achievable.

One of the tricky parts of weighting is which groups to weight. If you make the groups too broadly-defined, you don’t remove enough bias; if you make them too narrowly-defined, you end up with a few people getting really extreme weights, making the sampling error much larger than it should be. That’s what happened here: the survey had one person in one of its groups, and that person turned out to be unusual. But it gets worse.

The impact of the weighting was amplified because this is a panel survey, polling the same people repeatedly. Panel surveys are useful because they allow much more accurate estimation of changes in opinions, but an unlucky sample will persist over many surveys.

Worse still, one of the weighting factors used was how people say they voted in 2012. That sounds sensible, but it breaks one of the key assumptions about weighting variables: you need to know the population totals.  We know the totals for how the population really voted in 2012, but reported vote isn’t the same thing at all — people are surprisingly unreliable at reporting how they voted in the past.

The actual impact on polling aggregators such as 538 is probably pretty small, since they model and try to remove ‘house effects’ (differences between surveys). However, the poll does give aid and comfort to people who don’t want to believe the consensus results, and that is not helpful.

• A curriculum to help kids think critically about health claims has been developed — and is being evaluated in a randomised trial in Uganda (from Vox)
• Someone else (the website Grub Street) has fallen for the cheese addiction hoax. I wrote here about how the story makes no sense.  There’s a post by SciCurious that includes an interview with one of the people behind the actual research, talking about how the story just isn’t supported by her work. We still don’t seem to know who is pushing the hoax version.
• I was on RadioNZ’s Our Changing World, talking to Allison Ballance about means and medians
• Using mathematics (or statistics) to help with art repair:  Ingrid Daubechies talks about her work.
• From MBIE, an interactive map of NZ tourist numbers

This has been an urban legend in the UK — it’s true in Melbourne, though mostly because the Mt Waverley reservoir is a small storage buffer rather than main storage

The game that stops our city – Melbourne’s water use dropped dramatically during Saturday’s AFL Grand Final, and jumped during breaks #AFLGF pic.twitter.com/RzRV8f00na

— Melbourne Water (@MelbourneWater) October 3, 2016

Team Ratings for Round 9

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 62 matches played, 47 of which were correctly predicted, a success rate of 75.8%.
Here are the predictions for last week’s games.

Predictions for Round 9

Here are the predictions for Round 9. 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 October 14 2016.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of October 8 – 14 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.

(more…)

Q: Did you see that two cups of coffee a day will prevent dementia?

A: In Stuff? That’s not what it says.

Q: “Two cups of coffee a day can keep dementia at bay – research”

A: Read a bit further.

Q: Ok, so it’s just in women over 65, and two to three cups, and it’s a 36% reduction,  not as good as the headline says, but still pretty good, surely?

A: There’s a lot of uncertainty in that number

Q: So what’s the margin of error or whatever the medical folks call it?

A: According to the research paper a 95% confidence interval for the reduction goes from 1% to 44%. And it’s a reduction in rate, not in risk — it could easily be postponing rather than preventing dementia, even if it works.

Q: Was there a link to the paper?

A: No, but there was a link to the press release, and it linked to the paper.

Q: That interval. Why isn’t 36% in the middle of the interval?

A: I don’t know. The results in the abstract and tables of the paper give a hazard ratio of 0.74. I can think of two possibilities.  One is that the 36% isn’t based on the primary findings in the abstract but on a less well-described secondary analysis. The other is that someone subtracted 74% from 100% and got it wrong.

Q: Why is it just women over 65?

A: Because that’s who was in the study.

Q: So the coffee-drinking didn’t necessarily start at 65?

A: No, and it wasn’t necessarily coffee. It could have been tea or soda.

Q: Could they look at whether the coffee drinkers were different at the start of the study?

A: Yes — and they were.  The difference in their cognitive test scores stayed pretty much constant during the study, and the correlation with caffeine mostly goes away if you compare people starting out with the same test scores.

Q: So it might be that caffeine matters at an earlier age, not over 65?

A: And it might not matter — perhaps the people who drink a lot of caffeine were at lower risk for some other reason.

Q: Could it still be true?

A: It could.  It is in some lab-animal models of Alzheimer’s, but no-one really knows how relevant they are to human dementia

Q: Rats.

A: Yes, and mice.

Q: No, that was a colloquial exclamation expressing frustration, disappointment, or annoyance.

There’s a recent paper from Denmark finding that women, particularly young women, who used hormonal contraceptives were more likely also to be diagnosed with  depression.  The Guardian has a sensible story reporting on the paper (though given the topic it’s a pity the external experts they talked to were both men). There’s also an opinion piece, which conveys the importance of the issue but is clearly written by someone whose opinions were decided before the research came out. I was asked on Twitter what I thought.

One of the more difficult cases for science communication is where the evidence is neither negligible nor overwhelming, and that’s the situation here.  There’s nothing intrinsically unlikely about an effect on depression, and there are some ways that this study is very good, but there are also some limitations to the data that make the evidence weaker.

First, the good points. The study involved the entire Danish population over nearly 20 years, meaning that it was large enough to be fairly reliable on whether correlations are present or not, and also that it was comprehensive — it didn’t miss people out.  The data on who used hormonal contraceptives comes from the national health system and so should be accurate. The two definitions of depression — ‘prescribed anti-depressant drugs’ and ‘psychiatrist diagnosis of depression’ — will be measured reliably, and the decisions will have been taken by people who don’t have any particular view on the study question.  There’s information on timing, so we know the contraceptives were used before the depression. The associations are strong enough to care about, but not so strong as to be implausible. The analysis is well done given the data.

However, there are at least two alternative explanations for the correlation that aren’t ruled out by these data. The first is that the depression definitions require seeing a doctor and asking for (or at least accepting) treatment, and women who take hormonal contraceptives are probably more likely to see a doctor regularly.  The second explanation, which the researchers do consider, is that break-ups of relationships are a cause of depression, especially in younger people, and being in these relationships might be related to using hormonal contraceptives.  The researchers don’t believe this explanation, and they may be right, but their data don’t rule it out.

It’s not that either of these explanations is necessarily more likely than a direct effect of hormones, but if there weren’t alternative explanations the evidence would be stronger.  For example, if the researchers had been able to compare women using hormonal contraceptives just to those using non-hormonal contraceptives (eg copper IUDs and condoms), and had still seen the same correlation, the second explanation would be much less plausible and the evidence for a direct effect would be more convincing.

Also, if there were a straightforward hormonal explanation I would have expected different types of contraceptive to have stronger or weaker associations according to the dose of, say, progestins. In fact what they saw is that less commonly used contraceptives had stronger associations: weakest for the combined pills, stronger for progestin-only ‘mini-pills’ and stronger still for patch and implant methods. Again, this certainly doesn’t rule out a direct effect, but it weakens the evidence.

If a similar study were done in another country with different patterns of contraceptive use and found similar results, the evidence would become stronger. A study with fewer women but more detailed information on mental and emotional health — such as one of the birth-cohort studies — might be able to say more about what leads up to episodes of depression in young women and might be able to say something about who is at most risk. There’s still going to be uncertainty.

So. It’s hard to say for sure. There is definitely some evidence that hormonal contraceptives increase the risk of depression. If the effect is real, it’s useful to know that it seems to be largely in women under 20, largely in the first year of use, and might be worse for the ‘mini-pill’ than the traditional pill.  There’s a lot already known — good and bad — about hormonal contraceptives, but this research paper does add something.