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Since August 2018, I’ve been on an external data quality review panel looking at the Census 2018 data, as augmented by StatsNZ’s mitigation efforts. Our final report is out now (yesterday).  From the StatsNZ press release

The panel was convened by the Government Statistician in August 2018 to provide an independent, external review of the quality of 2018 Census data and to provide recommendations to the Government Statistician around improvements to census data quality. The eight-member panel includes experts on census methods, statistics, Māori data, demography, and equity.

It was the Government Statistician’s intention that the panel’s reports would be released publicly and unedited, as a matter of transparency, so all New Zealanders could see both the quality of the variables and the composition of the data.

Here’s the complete series

• Initial report
• Quality assessments for most of the variables, one by one
• Final report

The basic message is that the quality of the data varies enormously, both by variable and depending on what you want to use it for. Some of it is very good; some of it is not. You should read our assessments and the StatsNZ data quality information before doing anything you might later regret.

One of the problems with looking for health benefits of active transportation is that people who walk or cycle are self-selected weirdos. It’s a free country. You can’t just randomise people to owning a car or not.  You’d think.

As Alex Hutchinson, of the Globe and Mail reports, based on a research paper in the BMJ,

 Because of mounting congestion, Beijing has limited the number of new car permits it issues to 240,000 a year since 2011. Those permits are issued in a monthly lottery with more than 50 losers for every winner – and that, as researchers from the University of California Berkeley, Renmin University in China and the Beijing Transport Institute recently reported in the British Medical Journal, provides an elegant natural experiment on the health effects of car ownership.

The researchers interviewed a sample of 40,000 people across Beijing and asked them questions. Because of the lottery, the results should be more reliable than useful usual.

It’s not quite as simple as that.  First, people who respond to the survey may be unrepresentative (just over 20% responded). Second, the impact of winning the lottery seemed relatively small:

Our results indicate that those individuals winning a lottery permit to purchase a car reported transit use 45% lower than those who did not win…Differences in physical activity became apparent over time. About 2.6 years after winning, winners spent 7% less time walking or bicycling than losers. At 5.1 years the reduction in walking or bicycling rose to 42%.

This may be less surprising if you’ve been to Beijing and seen the traffic congestion. Anyway, the impact on physical activity was very small initially, though it did increase over time.

The main outcome variable measured was weight:

Average weight did not change significantly between lottery winners and losers.

If you look just at people over 50, and wait until five years after the lottery, there’s an estimated 10kg weight difference, but the statistical evidence is pretty weak and the uncertainty is large. The effect could easily be pretty much zero, and that’s without worrying about picking just one age group.

The basic message here is that it’s hard to do experiments on driving — even in one of the world’s biggest cities, the data end up being consistent with anything from no effect to a huge effect.

There have been some fairly large fluctuations in the reported number of cases of COVID-19, the new coronavirus, in China, as the authorities change how they define cases.  That’s not as dodgy as it might sound.

We can divide the population into two groups according to how they feel: do they have symptoms consistent with COVID-19 infection or not.  We can divide them into three groups according to viral testing: positive, negative, not tested yet.  Outside the outbreak area we could also divide people according to whether they had a plausible exposure or not, but at the centre of the outbreak it makes sense to assume basically anyone could have been exposed.  We end up with six groups.

• The no-symptoms, negative test group clearly shouldn’t be counted as cases.
• The symptoms, positive test group clearly are cases.

Then it gets harder:

• The symptoms, no-test group will be mixed.  Many of them will have COVID-19 infection, but others will just have some other influenza-like illness. The likelihood that they are cases will vary according to exactly what symptoms they have.  Most of these people are being tested for the virus, but testing for a new virus is relatively slow and takes expertise, and the testing labs are backed up. The subset of people with lower respiratory tract infection confirmed by chest imaging (x-ray) were recently added to the official case count, but only if they are in Hubei province, China.
• The symptoms, negative test group are probably not cases of COVID-19.
• The no-symptoms, positive test group are probably cases, but since few asymptomatic people are being tested, they will be a small and unrepresentative subset of the asymptomatic cases. I have one source that says these were recently subtracted from the count
• The no-symptoms, no-test group includes nearly everyone, including most of the asymptomatic (or mildly symptomatic) cases.

Who you want to count depends on what you want to do with the data.

Team Ratings for Round 4

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 21 matches played, 12 of which were correctly predicted, a success rate of 57.1%.
Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. 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 60 matches played, 41 of which were correctly predicted, a success rate of 68.3%.
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 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 76 matches played, 58 of which were correctly predicted, a success rate of 76.3%.
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.

Team Ratings for Round 11

I missed posting predictions for this round thinking that there was a longer break between games. Because I use an algorithm and code and no subjective analysis, these predictions are exactly what I would have posted and I am posting them now for completeness.

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, 54 of which were correctly predicted, a success rate of 77.1%.
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 3

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 14 matches played, 8 of which were correctly predicted, a success rate of 57.1%.
Here are the predictions for last week’s games.

Predictions for Round 3

Here are the predictions for Round 3. 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 2

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 7 matches played, 5 of which were correctly predicted, a success rate of 71.4%.
Here are the predictions for last week’s games.

Predictions for Round 2

Here are the predictions for Round 2. 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.

Back in early December, I wrote about a political ad authorised by Simon Bridges, showing the price of fuel.

As I said the, the numbers do not remotely match the graph.  A graph using those numbers would look more like

Dylan Reeve and other people complained to the Advertising Standards Authority, both about the graph itself and about the choice of numbers, which (in his opinion and mine) was cherrypicked in a misleading way.

The ASA decided (in a split decision) that the graphic was not misleading

The majority said the data displayed was correct which saved the hyperbolic graphic from being misleading, given the political medium used and the principles of advocacy advertising.

I believe this is decision is bad in terms of norms for mainstream political advertising, and that it’s likely to be factually incorrect as to the impact of the graphic.

The cherrypicked numbers are misleading, but they are misleading in a way that is, sadly, routine in political advertising.  I’ve written about examples from both parties here since StatsChat started. My starting point for any political advocacy involving numerical comparisons is always that the numbers are likely to be correct as quoted, but chosen to mislead. Given the established norms,  I can understand the ASA not wanting to get involved.

The distorted graph, on the other hand, seems to be new.  I was genuinely surprised at the extent of the distortion — well beyond common tricks of perspective or false baseline.

If writing the numbers on a misleading graph was enough to stop it being misleading, there would be no point having data graphics.  The whole point of data graphics is that they provide a clearer and more forceful impression of the data than just tabulating the numbers.  Misleading graphs are misleading.