Posts filed under General (2814)

An article from ABC News in Adelaide, South Australia, describes incidents where fencing wire was strung across a bike path.  According to police

The riders were travelling about 35 kilometres per hour and fell from their bikes. Two suffered minor injuries, while the third was not injured.

Police said each of their bicycles were severely damaged.

That sounds at first like extraordinary good luck: if you come off a bike at 35 km/h and your bike was wrecked, you’d expect to be damaged too.  I think the problem, as with a lot of discussions of road crashes, is the official assessment metrics for injuries.  In South Australia, according to this and similar documents:

Serious Injury – A person who sustains injuries and is admitted to hospital for a duration of at least 24 hours as a result of a road crash and who does not die as a result of those injuries within 30 days of the crash.

Minor Injury – A person who sustains injuries requiring medical treatment, either by a doctor or in a hospital, as a result of a road crash and who does not die as a result of those injuries with 30 days of the crash.

A broken bone leading to substantial disability might easily not be a Serious Injury, and several square inches of road rash may well not be even a Minor Injury. (New Zealand has the same definition of a “serious” injury is one that gets you admitted to hospital for an overnight stay, but doesn’t have restrictive standards for minor injury)

It’s not that these definitions are necessarily bad for collecting data — there’s a lot to be said for a definition that’s administratively checkable — but it does mean you might want to translate from officialese to ordinary language when reporting individual injuries or aggregated statistics to ordinary people.

Update: One of the cyclists in the first group has talked to the ABC. One of the “minor injuries” required five stitches.

Q: Did you see that coffee only works if you drink it in the morning?

A: Works?

Q: Reduces your risk of heart disease

A: That’s … not my top reason for drinking coffee. Maybe not even top three.

Q: But is it true?

A: It could be, but probably not

Q: Mice?

A: No, this is in people. Twenty years of survey responses from the big US health survey called NHANES. They divided people into three groups depending on their coffee consumption on the day of their dietary interview: no coffee, coffee only between 4am and 11:59am, and coffee throughout the day

Q: Couldn’t there be other differences between people who drink coffee in the morning and afternoon? Like, cultural differences or age or health or something?

A: Yes, the researchers aimed to control for these statistically: differences in age, sex, race and ethnicity, survey year, family income, education levels, body mass index, diabetes, hypertension, high cholesterol, smoking status, time of smoking cessation, physical activity, Alternative Healthy Eating Index, total calorie intake, caffeinated coffee intake, and decaffeinated coffee intake, tea intake, and caffeinated soda intake, short sleep duration, and difficulty sleeping

Q: Um. Wow?

A: I mean, it’s a good effort. One of the real benefits of NHANES is it measures so much stuff.  On the other hand a lot of these things aren’t measured all that precisely, and it’s not like you have a lot of people left when you chop up the sample that many ways.  And the evidence for any difference is pretty marginal

Q: What’s their theory about how the coffee supposed to be working?

A: The Guardian and BBC versions of the story quotes experts who thinks it’s all about coffee disrupting sleep

Q: That sounds kind of plausible — but didn’t you say they adjusted for sleep?

A: Yes, so if the adjustments work it isn’t sleep

Q: Should we be campaigning for cafes to close early, like the new Auckland alcohol regulations?

A: It’s much too early for that, and in any case there isn’t any real suggestion coffee is harmful after noon.  It might be worth someone repeating the research in a very different population from the US but where people still drink coffee. There are plenty of those.

Q: And what about advice to readers on their coffee consumption?

A: The standard StatsChat advice: if you’re drinking coffee in the morning primarily for the good of your heart, you may be doing it wrong.

• The top baby names from New Zealand last year are out.  As we’ve seen in the past, the most-common names keep getting less common. “Noah” came top for boys, with only 250 uses, and “Isla” for girls, with only 190 uses.
• The Daily Mail (because of course) has something purporting to be a map of penis sizes around the world, credited to this site, which gives no sources for the data. Wikipedia points out that a lot of data on this topic is self-reported claims. Wikipedia (possibly NSFW) notes thatMeasurements vary, with studies that rely on self-measurement reporting a significantly higher average than those with a health professional measuring. Even when it’s measured, it tends to be on volunteer samples, and there isn’t good standardisation of measurement protocols across sites.
• If you live in one of these Aussie suburbs buy a lottery ticket NOW, says the headline on MSN.com, from the Daily Mail (Australia version).  This is a much more extreme headline than the NZ versions I usually complain about, and the text is more measured. Of course, there are two reasons why a suburb will see more lottery wins. The first is just chance, which doesn’t project into the future like that. The second is that these are suburbs where more money is lost on the lottery. Those trends probably will continue, but lottery advertising stories never seem to print the amounts lost on lotto.
• We’ve seen a number of times that salary/wage ranges generated from advertising at Seek are not very similar to those reported from actual payments by StatsNZ.  This is worse: via Carl Bergstrom and Eduardo Hebkost, on Bluesky, apparently ziprecruiter.com will (in the US at least; not in NZ) give you salaries for any job you ask about, if you just forge a URL pointing to where the graph should be
  

Team Ratings for Week 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 69 matches played, 51 of which were correctly predicted, a success rate of 73.9%.
Here are the predictions for last week’s games.

Predictions for Week 10

Here are the predictions for Week 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.

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 50 matches played, 32 of which were correctly predicted, a success rate of 64%.
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 15

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 98 matches played, 76 of which were correctly predicted, a success rate of 77.6%.
Here are the predictions for last week’s games.

Predictions for Round 15

Here are the predictions for Round 15. 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.

Ok, this is a bit late, but I didn’t see the poll (in a physical Sunday Star-Times) until this week.  An established Australian polling firm, Freshwater Strategy, have been doing polls here, too.  Stuff reports that the poll (also, at the Post)

…reveals 37% of New Zealand voters have seriously considered emigrating to Australia in the past 12 months.

By comparison, of Australian voters, only 8% have considered moving to New Zealand, including just 1% who have spent time looking into it.

If you don’t think too carefully, that gives the impression of a giant sucking sound and the lights going out in New Zealand.  Australia is a lot larger than New Zealand, though.  If 8% of people in Australia moved to New Zealand and 37% of people in New Zealand moved to Australia, the population of New Zealand would go up, not down.  The total populations are about 5 million and about 27 million. Of those, about 3.6 million are enrolled to vote in NZ and nearly 18 million enrolled to vote in Australia, so 37% of NZ voters is 1.3 million and 8% of Oz voters is 1.44 million.

Another useful comparison number is that the largest ever number of people migrating out of NZ to all destinations, not just Australia, over any 12 months is about 130,000, a tenth of the ‘seriously considered’ number. A lot of people (apparently) seriously consider a lot of things they don’t end up doing.

The other important aspect of the story is the estimates quoted for small subpopulations.  Overall, the poll claims a maximum margin of error of about  3 percentage points. That’s for the population as a whole. Proportions are given for different age groups, including 18-34 year olds, people earning more than $150,000, and voters for Te Pāti Māori.  We aren’t told the uncertainty in these numbers, but it’s obviously higher.  About 1/3 of adults are 18-34, about 5% earn over $150k (IRD spreadsheet), and about 3% voted for Te Pāti Māori.  The maximum margin of error for subpopulations this big would be 5, 13, and 17 percentage points respectively, assuming equal sampling.   You can’t easily learn much about wealthy people or Pāti Māori voters just by contacting random people throughout the country — and the assumption that you can make your sample representative by reweighting gets increasingly dodgy.

Team Ratings for Week 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 69 matches played, 51 of which were correctly predicted, a success rate of 73.9%.
Here are the predictions for last week’s games.

Predictions for Week 10

Here are the predictions for Week 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.

Team Ratings for Round 14

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 91 matches played, 71 of which were correctly predicted, a success rate of 78%.
Here are the predictions for last week’s games.

Predictions for Round 14

Here are the predictions for Round 14. 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 45 matches played, 29 of which were correctly predicted, a success rate of 64.4%.
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.