Stats Chat

So, how did the polls do this time? First, the main result was predicted correctly: either side needs a coalition with NZ First.

In more detail, here are the results from Peter Ellis’s forecasts from the page that lets you pick coalitions.

Each graph has three arrows. The red arrow shows the 2014 results. The blue/black arrow pointing down shows the current provisional count and the implied number of seats, and the horizontal arrow points to Graeme Edgeler’s estimate of what the special votes will do (not because he claims any higher knowledge, but because his estimates are on a web page and explain how he did it).

First, for National+ACT+UnitedFuture

Second, for Labour+Greens

The result is well within  the uncertainty range of the predictions for Labour+Greens, and not bad for  National. This isn’t just because NZ politics is easy to predict: the previous election’s results are much further away. In particular, Labour really did gain a lot more votes than could reasonably have been expected a few months ago.

Update: Yes, there’s a lot of uncertainty. And, yes, that does  mean quoting opinion poll results to the nearest 0.1% is silly.

Unless someone says something really annoyingly wrong about polling in the next few days, I’m going to stop commenting until Saturday night.

Some final thoughts:

• The election looks closer than NZ opinion polling is able to discriminate. Anyone who thinks they know what the result will be is wrong.

• The most reliable prediction based on polling data is that the next government will at least need confidence and supply from NZ First. Even that isn’t certain.

• It’s only because of opinion polling that we know the election is close. It would be really surprising if Labour didn’t do a lot better than the 25% they managed in the 2014 election — but we wouldn’t know that without the opinion polls.

Right from the start of StatsChat we’ve looked at stories about how men or women have more sexual partners. There’s another one in the Herald as a Stat of the Week nomination.

To start off, there’s the basic adding-up constraint: among exclusively heterosexual people, or restricted to opposite-sex partners, the two averages are necessarily identical over the whole population.

This survey (the original version of the story is here) doesn’t say that it just asked about opposite-sex partners, so the difference could be true.  On average, gay men have more sexual partners and lesbians have fewer sexual partners, so you’d expect a slightly higher average for all men than for all women.  Using binary classifications for trans and non-binary people will also stop the numbers matching exactly.

But there are bigger problems. First, 30% of women and 40% of men admit this is something they lie about. And while the rest claim they’ve never lied about it, well, they would, wouldn’t they?

And the survey doesn’t look all that representative.  The “Methodology” heading is almost entirely unhelpful — it’s supposed to say how you found the people, not just

We surveyed 2,180 respondents on questions relating to sexual history. 1,263 respondents identified as male with 917 respondents identifying as female. Of these respondents, 1,058 were from the United States and another 1,122 were located within Europe. Countries represented by fewer than 10 respondents and states represented by fewer than five respondents were omitted from results.

However, the sample is clearly not representative by gender or location, and the fact that they dropped some states and countries afterwards suggests they weren’t doing anything to get a representative sample.

The Herald has a bogus clicky poll on the subject. Here’s what it looks like on my desktop

On my phone it gets a couple more options visible, but not all of them. It’s probably less reliable than the survey in the story, but not by a whole lot.

This sort of story can be useful in making people more willing to talk about their sexual histories, but the actual numbers don’t mean a lot.

• During the Cold War, there were a few occasions where a nuclear war could easily have started if one person hadn’t got in the way. One of those people was Stanislav Petrov. He died this week.

• I saw a pharmacy in Ponsonby advertising “Ultrasound bone density screening for all ages”. There’s no way screening for osteoporosis makes sense ‘for all ages’, even if it was free (which it isn’t).

• As I’ve mentioned a few times, the UK has an independent Statistics Authority whose chair is supposed to monitor and rebuke misuses of official statistics. The chair, Sir David Norgrove, criticised Boris Johnson over the £350m “savings” from Brexit he has kept repeating. We don’t have anything similar, sadly.

• If you’re interested in the history of data journalism, you could do worse than reading Alberto Cairo’s PhD thesis. Dr Cairo is a former data journalist, current professor of visual journalism at the University of Miami, and one of next year’s Ihaka Lecture speakers here in Auckland.

• Janelle Shane has a blog with examples of neural networks generalising from a wide range of inputs (recipes, hamster names, craft beers). Her current post is on D&D spell names, and shows the importance of a large input set for these networks: would you prefer your character to cast “Plonting Cloud” or “Wall of Storm”?

• Roger Peng on communication in data science
• Al Gore’s An Inconvenient Sequel: Truth to Power, has really weird IMDB ratings — because sometimes it’s not about the actual movie. 

• Kieran Healy, of Duke University, has an online book Data Visualization for Social Science. Yes, if you think you recognise the name, it’s him.

• The American Statistical Association and the New York Times are partnering in a new monthly feature, “What’s Going On in This Graph?”

• XKCD on colour. And ensemble forecast graphs.

It’s pretty obvious that Bon Appétit has just confused averages and totals here.

So, what is the average? There were about 75 million millennials in the US in 2016 (we can probably assume  Bon Appétit doesn’t care about other countries), so we’re looking at $1280/year, or about $25/week. Which actually seems pretty low as an average.  The US as a whole spent $1.46 trillion on food and beverages in 2014, which is about $4500/person/year or about $87/week.

As with so much generation-mongering, asking about the facts is missing the intended purpose of the story, which is to recycle some stereotypes about lazy/wasteful youth.

The story links to another, about a new book “Generation Yum”

Turow characterized the quintessential Millennial experience this way: “You got into a top tier high school, you hustled through college—you’ve done everything society told you—and you’re not rewarded. 

When “get into a top-tier high school” is a quintessential generational experience it’s clear we’re not even trying to go beyond unrepresentative stereotypes.  In which case, hold the numbers.

Team Ratings for the Preliminary Finals

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 198 matches played, 119 of which were correctly predicted, a success rate of 60.1%.
Here are the predictions for last week’s games.

Predictions for the Preliminary Finals

Here are the predictions for the Preliminary Finals. 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.

Team Ratings for Round 6

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 38 matches played, 24 of which were correctly predicted, a success rate of 63.2%.
Here are the predictions for last week’s games.

Predictions for Round 6

Here are the predictions for Round 6. 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.

Team Ratings for Round 11

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 30 matches played, 20 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Predictions for Round 11

Here are the predictions for Round 11. 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.

There’s a new Herald story with the lead

Artificial intelligence (AI) can identify Alzheimer’s disease 10 years before doctors can discover the symptoms, according to new research.

The story doesn’t link (even to the Daily Mail). Before we get to that, regular StatsChat readers will have some idea of what to expect.

Early diagnosis for Alzheimer’s is potentially useful when designing clinical trials for new treatments, and eventually will be useful for early treatment (when we get treatments that work).  But not yet.  It’s also not as much of a novelty as the story suggests. Candidate tests for early diagnosis are appearing all over the place (here’s seven of them).

Second, you’d expect that the accuracy of the test and its degree of foresight to have been exaggerated — and the story confirms this.

Following the training, the AI was then asked to process brains from 148 subjects – 52 were healthy, 48 had Alzheimer’s disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer’s disease two and a half to nine years later.

That is, the early diagnosis wasn’t of people without symptoms, it was of people whose symptoms had led to a diagnosis but didn’t amount to dementia

The Herald doesn’t link, but Google finds a story at New Scientist, and they do link. The link is to the arXiv preprint server. That’s unusual: normally this sort of story is either complete vapour or is based on an article in a research journal.  This one is neither: it’s a real scientific report, but one that hasn’t yet been published — it’s probably undergoing peer review at the moment.

Anyway, the preprint is enough to look up the accuracy of the test. The sensitivity was high: nearly all Alzheimer’s cases and cases of Mild Cognitive Impairement were picked up. The specificity was terrible: more than 1/4 of people tested would receive a false positive diagnosis.

It’s possible that this test can be re-tuned into a genuinely useful clinical tool. As published, though, it isn’t even close.

Q: Did you see icecream for breakfast may improve mental performance?

A: Pigs may fly

Q: But it’s a STUDY

A: That’s actually one of the questions left unresolved.

Q: Just follow the link. The International Business Times links to their source.

A: That link is to a Japanese news site. And it’s 404.

Q: Already? The tweet was just from this weekend.

A: The story is from November last year.

Q: But there’s a professor! Isn’t he real? Can’t you look at his publications.

A: Yes, he’s real. And he has publications. And they aren’t about icecream for breakfast.

Q: Back to the icecream. It could still be true, even if the data aren’t published, right?

A: Sure. In fact there’s a fair chance that, compared to no breakfast, icecream could improve mental performance.

Q: The comparison was to not eating anything?

A: It was compared to a glass of cold water.

Q: So, what does this tell us?

A: 2017 must be a slow news year.