Stats Chat

You’ve probably seen the 2016 US election results plotted by county, as in this via Brilliant Maps

It’s not ideal, because large, relatively empty counties take up a lot of space but represent relatively few people.  It’s still informative: you can see, for example, that urban voters tended to support Clinton even in Texas.  There are also interesting blue patches in rural areas that you might need an atlas to understand.

For most purposes, it’s better to try to show the votes, such as this from the New York Times, where the circle area is proportional to the lead in votes

You might want something that shows the Electoral College votes, since those are what actually determines the results, like this by Tom Pearson for the Financial Times

Or, you might like pie charts, such as this one from Lisa Charlotte Rost

These all try to improve on the simple county map by showing votes — people — rather than land. The NYT one is more complex than the straightforward map; the other two are simpler but still informative.

Or, you could simplify the county map in another way. You could remove all the spatial information from within states — collecting the ‘blue’ land into one wedge and the ‘red’ land into another — and not add anything. You might do this as a joke, to comment on the President’s use of the simple county map

The problem with the Internet, though, is that people might take it seriously.  It’s not completely clear whether Chris Cillizza was just trolling, but a lot of people sure seem to take his reposting of it seriously.

Q: Did you see the Jagerbombs ‘as bad as taking cocaine’ headline in the Herald?

A: Doesn’t sound all that plausible. What’s a Jagerbomb? Is it like a Molotov cocktail?

Q: Apparently it’s Red Bull with Jägermeister.

A: Well, I don’t think I’m in any danger of drinking that.  How did you get the little dotty things over the ‘a’, by the way?

Q: If you hold down the ‘a’ key they pop up as an option (on a Mac). Like the Māori long vowels. But we nearly digress. Is a Jagerbomb really that dangerous?

A: Well, the story goes on to quote the researcher saying “I wondered if they were having a similar impact but to a lesser degree”

Q: And are they?

A: For suitable definitions of ‘same’ and ‘lesser’. And ‘were’.

Q: ಠ_ಠ

A: The research (no link given) looked at whether combining alcohol and energy drinks led to people getting injured more.

Q: That’s not the first thing you think of as a risk of taking cocaine. And how did they do the comparison? Recruit undergraduates with poor taste and give them Jagerbombs?

A: No

Q: You’re not going to say ‘mice’, are you?

A: No, what they did was go to the library and find all the previous studies of alcohol and energy drinks and injury, to review and summarise them.

Q: Is that useful?

A: It can be very useful. You can’t keep all that information in your head even if you’re a qualified Jagerbombologist.  Systematic reviews are a great innovation in modern medical research.

Q: So, how does the risk of injury compare to the risk with cocaine?

A: They didn’t look at that.

Q: Oh. Ok. So how big is the risk?

A: They didn’t come up with anything quantitative, because the previous research studies hadn’t been done in a similar enough way

Q: Did they come up with anything?

A: Yes, the results “suggest support for a relationship between increased risk of injury and [alcohol with energy drink] use”

Q: That’s not all that strong.

A: No.

Q: Was there a stronger relationship than just with alcohol on its own?

A: They say “some studies did not differentiate between injuries occurring in alcohol-only sessions relative to AmED sessions, making a comparison of the risk of injury between alcohol and AmED use impossible.”

Q: Even if there is a difference, couldn’t it be that the sort of people who drink Jagerbombs or rum and Coke are different from people who drink beer, or cosmopolitans, or Sauv Blanc?

A: “Although the results remain mixed in terms of whether impulsivity or risk taking may moderate the relationship between AmED use and injury risk, there is enough evidence to warrant further exploration.”

Q: That all seems very reasonable.

A: The actual story isn’t too bad, either. Just the web-front-page headline.

Q: Wait, doesn’t the headline punctuation imply ‘as bad as taking cocaine’ is a quote? When it totally isn’t?

A: Yes. Yes, it does.

• “What CPE—and the field—needs now are analysts. Lots and lots of analysts. And we, at least, are hiring DataNerds who want to be JusticeNerds™. With departments now coming in by the state-load, we are inundated with confidential data that needs to be interrogated so that we can answer some of the most fundamental questions in policing” from Phil Goff (no, not that one) at the Center for Policing Equity, via mathbabe.org

• If someone claims your female developers are promoted less because they’re treated worse at code review, and you say “no, they’re treated worse because they’re more junior”, you’ve made the basic causal-inference error of conditioning on an intermediate consequence of your input variable.  Felix Salmon on Facebook’s example

• “For too long social welfare has muddled along with bipartisan policies like shouting at the jobless or not helping people with mental health issues, without really checking if those methods work.” This, from, Lyndon Hood, isn’t going where you might expect.  It’s unfair, but not completely unfair.

• Another reason ‘breakthrough’ science stories may be misleading: there was a research paper claiming fish preferentially eat microplastic pollution and are serious harmed by it. It has been retracted. There are allegations of deliberate fraud; the data were certainly not made available as the journal’s policy demanded.  If you remember a story on this, go back and see if the same media outlet covers the retraction.

• I’ve written a few times about the bogus claim that the typical Kiwi pays no “net tax”.  In the other direction, there were stories about  “Tax Freedom Day” this week, on the basis of 34.8% of income going in tax. Yeah, nah.

• Derek Lowe writes about a new analysis looking at solanezumab, Eli Lilly’s failed treatment for Alzheimer’s. The analysis claims that if the drug had been approved based on the early, weak signals of benefit, the cost to the US government would have been about ten billion dollars over the past four years. That would pay for a lot of trials, or for a lot of other improvements to dementia care.

• There’s publication bias in research on stock-market patterns. Because of course there is.

• Somewhat depressingly, someone has looked what people write when they apply for loans at peer-to-peer lending site Prosper (like Harmoney here in NZ). The phrases most indicative of the loan defaulting: “God, promise, will pay, thank you, hospital.”   (via Matt Levine, who isn’t surprised)

This was almost too good to check

Know there’s a lot of news out there about France right now, but I just found out a pie graph is called a Camembert.

— Lauren Collins (@laurenzcollins) May 8, 2017

And this was too good to ask if it’s a joke:

(it is)

Team Ratings for Round 12

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 87 matches played, 67 of which were correctly predicted, a success rate of 77%.
Here are the predictions for last week’s games.

Predictions for Round 12

Here are the predictions for Round 12. 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 May 12 2017.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of May 6 – 12 2017 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…)

Keith Ng drew my attention on Twitter to an ad from Labour saying “Under National, the number of young people not earning or learning has increased by 41%”.

When you see this sort of claim, you should usually expect two things: first, that the claim will be true in the sense that there will be two numbers that differ by 41%; second, that it will not be the most informative summary of the data in question.

If you look on Infoshare, in the Household Labour Force Survey, you can find data on NEET (not in education, employment, or training).  The number was 64100 in the fourth quarter of 2008, when Labour lost the election.  It’s now (Q1, 2017) 90800, which is, indeed, 41% higher.  Let’s represent the ad by a graph:

We can fill in the data points in between:

Now, the straight line doesn’t look as convincing.

Also, why are we looking at the number, when population has changed over this time period. We really should care about the rate (percentage)

Measuring in terms of rates the increase is smaller — 27%.  More importantly, though, the rate was even higher at the end of the first quarter of National’s administration than it is now.

The next thing to notice is the spikes every four quarters or so: NEET is higher in the summer and lower in the winter because of the school  year.  You might wonder if StatsNZ had produced a seasonally adjusted version, and whether it was also conveniently on Infoshare…

The increase is now 17%

But for long-term comparisons of policy, you’d probably want a smoothed version that incorporates more than one quarter of data. It turns out that StatsNZ have done this, too, and it’s on Infoshare.

The increase is, again 17%. Taking out the seasonal variation, short-term variation, and sampling noise makes the underlying pattern clearer.  NEET increased dramatically in 2009, decreased, and has recently spiked. The early spike may well have been the recession, which can’t reasonably be blamed on any NZ party.  The recent increase is worrying, but thinking of it as trend over 9 years isn’t all that helpful.

Given the discussions of immigration in the past weeks, I decided to look for some historical data.  Stats NZ has a report “A Century of Censuses”, with a page on ‘proportion of population born overseas.” Here’s the graph

The proportion of immigrants has never been very low, but it fell from about 1 in 2 in the late 19th century to about 1 in 6 in the middle of the 2oth century, and has risen to about 1 in 4 now. The increase has been going on for the entire lifetime of any NZ member of Parliament; the oldest was born roughly at Peak Kiwi in the mid-1940s.

Seeing that immigrants have been a large minority of New Zealand for over a century doesn’t necessarily imply anything about modern immigration policy — Hume’s Guillotine, “no ought deducible from is,” cuts that off.  But I still think some people would find it surprising.

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 79 matches played, 60 of which were correctly predicted, a success rate of 75.9%.
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.

Team Ratings for Round 10

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 72 matches played, 40 of which were correctly predicted, a success rate of 55.6%.
Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 10. 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 May 5 2017.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 29 – May 5 2017 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…)