Posts filed under General (3152)

Because of changes at WordPress, the Stat of the Week competition has been eating the URLs you submitted.

Um.

Sorry.

We’ve fixed it now.

Three more sites have fallen for the cheese addiction hoax

• Popsugar.com
• India.com
• Daily Times (of Pakistan)

As you may remember, this story is very very loosely based on real research from the University of Michigan. However, the hoax version misrepresents which foods were most addictive and makes up an explanation based on the milk protein casein that isn’t mentioned in the real research at all.

The reason I’m calling this a hoax is that it wasn’t the fault of the researchers, their institution, or the journal, and it’s obvious to anyone who makes any attempt to scan the research paper that it doesn’t support the story. It isn’t an innocent mistake, and it isn’t a simple exaggeration like most misleading health science stories.

There’s a good post at Science News describing what was actually found.

At a conference earlier this week, a research team from Microsoft described a computer system for speech transcription. For the first time ever, this system did better than humans on a standard set of recordings.

What’s more impressive — and StatsChat relevant — is that this computer system does not understand anything about the conversations it writes down. The system does not know English, or any other human language, even in the sense that Siri does.

It has some preconceived notions about what tends to follow a particular word, pair of words, or triple of words, and about what sequences of sounds tend to follow each other, but nothing about nouns or verbs or how colorless green ideas sleep. As with modern image recognition, the system is just based on heaps and heaps of data and powerful computers.  It’s computing and statistics, not linguistics.

In a comment to a post at Language Log, the linguist Geoffrey Pullum says

I must confess that I never thought I would see this day. In the 1980s, I judged fully automated recognition of connected speech (listening to connected conversational speech and writing down accurately what was said) to be too difficult for machines, far more difficult than syntactic and semantic processing (taking an error-free written sentence as input, recognizing which sentence it was, analysing it into its structural parts, and using them to figure out its literal meaning). I thought the former would never be accomplished without reliance on the latter.

There are many problems where enough data is not available to construct a model with no understanding of the problem. There won’t be a shortage of work for human statisticians or linguists any time soon. But there are problems where brute force and ignorance works, and they aren’t always the ones we expect.

Team Ratings for the Mitre 10 Cup Semi-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 70 matches played, 50 of which were correctly predicted, a success rate of 71.4%. Here are the predictions for last week’s games.

Predictions for the Mitre 10 Cup Semi-Finals

Here are the predictions for the Mitre 10 Cup Semi-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 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.

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.

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.