Stats Chat

Jarrod Gilbert had a piece in the Herald about prisons

Fifty per cent of the prison population is Maori. It’s a fact regularly cited in official documents, and from time to time it garners attention in the media. Given they make up 15 per cent of the population, it’s immediately clear that Maori incarceration is highly disproportionate, but it’s not until the numbers are given a greater examination that a more accurate perspective emerges.

The numbers seem dystopian, yet they very much reflect the realities of many Maori families and neighbourhoods.

to know what he was talking about, qualitatively. I mean, this isn’t David Brooks.

It turns out that while you can’t easily get data on ethnicity by age in the prison population, you can get data on age, and that this is enough to get a good idea of what’s going on, using what epidemiologists call “indirect standardisation”.

Actually, you can’t even easily get data on age, but you can get a graph of age:

and I resorted to software that reconstructs the numbers.

Next, I downloaded Māori population estimates by age and total population estimates by age from StatsNZ, for ages 15-84.  The definition of Māori won’t be exactly the same as in Dr Gilbert’s data. Also, the age groups aren’t quite right because we’d really like the age when the offence happened, not the current age.  The data still should be good enough to see how big the age bias is. In these age groups, 13.2% of the population is Māori by the StatsNZ population estimate definition.

We know what proportion of the prison population is in each age group, and we know what the population proportion of Māori is in each age group, so we can combine these to get the expected proportion of Māori in the prison population accounting for age differences. It’s 14.5%.  Now, 14.5% is higher than 13.2%, so the age-adjustment does make a difference, and in the expected direction, just not a very big difference.

We can also see what happens if we use the Māori population proportion from the next-younger five-year group, to allow for offences being committed further in the past. The expected proportion is then 15.3%, which again is higher than 13.2%, but not by very much. Accounting for age, it looks as though Māori are still more than three times as likely to be in prison as non-Māori.

You might then say there are lots of other variables to be looked at. But age is special.  If it turned out that Māori incarceration rates could be explained by poverty, that wouldn’t mean their treatment by society was fair, it would suggest that poverty was how it was unfair. If the rates could be explained by education, that wouldn’t mean their treatment by society was fair; it would suggest education was how it was unfair. But if the rates could be explained by age, that would suggest the system was fair. They can’t be.

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 70 matches played, 46 of which were correctly predicted, a success rate of 65.7%.
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.

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 64 matches played, 35 of which were correctly predicted, a success rate of 54.7%.
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.

There’s a headline in the Independent “If you think more celebrities are dying young this year, you’re wrong – it’s just a trick of the mind“. And, in a sense, Ben Chu is right. In a much more important sense, he’s wrong.

He argues that there are more celebrities at risk now, which there are. He says a lot of these celebrities are older than we realise, which they are. He says that the number of celebrity deaths this year is within the scope of random variation looking at recent times, which may well be the case. But I don’t think that’s the question.

Usually, I’m taking the other side of this point. When there’s an especially good or especially bad weekend for road crashes, I say that it’s likely just random variation, and not evidence for speeding tolerances or unsafe tourists or breath alcohol levels. That’s because usually the question is whether the underlying process is changing: are the roads getting safer or more dangerous.

This time there isn’t really a serious question of whether karma, global warming, or spiders from Mars are killing off celebrities.  We know it must be a combination of understandable trends and bad luck that’s responsible.  But there really have been more celebrities dying this year.   Prince is really dead. Bowie is really dead. Victoria Wood, Patty Duke, Ronnie Corbett, Alan Rickman, Harper Lee — 2016 has actually happened this way,  it hasn’t been (to steal a line from Daniel Davies) just a particularly inaccurate observation of the underlying population and mortality patterns.

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 April 29 2016.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 23 – 29 2016 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…)

From a 2014 interview of Randall Munroe (XKCD) at 538,

We’re always seeing things like, “This canal project will require 1.15 million tons of concrete.” It’s presented as if it should mean something to us, as if numbers are inherently informative. So we feel like if we don’t understand it, it’s our fault.

[…]

 …Or is this just easy, space-filling trivia? A good rule of thumb might be, “If I added a zero to this number, would the sentence containing it mean something different to me?” If the answer is “no,” maybe the number has no business being in the sentence in the first place.

via Jenny Bryan

• “Your friendly guide to colors in data visualisation” by Lisa Charlotte Rost.  (via Karl Broman)

• An example of bad forms design. 73% of members of the “American Indepedent Party” in California didn’t realise they were members of a party. They won’t be able to vote in the Democratic primary, though unaffiliated voters will be able to.  This ‘73%’ is also an example of the denominator mattering: the errors are estimated at 73% of AIP members but only 12% of independents

• Herald (Daily Mail) headline “Meditation can knock 7 years off age of your brain”. Text: “those who meditate may lead healthier lifestyles in general. It is also possible that some inherent difference in brain structure makes some people more likely to take up meditating. Those studied had practised various types of traditional meditation for an average of 20 years.“

• Amazon has been distinctive for making the same prices available to rich and poor Americans. But the same-day free delivery service is becoming an exception. Bloomberg looks at why (with graphics) (via Harkanwal Singh)

• Maps of electorate-level odds for the Australian election, with an interesting attempt to solve the problem of a continent made up mostly of empty space

• A data proofreading app designed for data journalists (via Kristin Henry)

Another nice Herald interactive, this time of housing affordability.

Affordability comes in two parts: down payment and monthly mortgage costs. The affordability index from Massey University looks at monthly payments; this one looks at the 20% down payment.

The difference between Auckland and the rest of the country is pretty dramatic, but there are other things to see. Above, the centre of Auckland is much less expensive than the rest of the city: 75% of properties are valued at under $500,000 by CoreLogic.  That’s the apartments, but they mostly aren’t the sort of apartments people are planning to stay in long-term.

Another interesting feature for Auckland is that the neighbourhoods really are ordered in price — you don’t see the spatial trends changing as you move the slider, so there aren’t areas where the low-end houses are especially cheap and the high-end houses especially expensive.

You can also see the difficulty of relating valuations to prices. In Point Chev, the valuations say 70% of homes are valued at over $1 million. On the other hand, the median sale price is $990,00, so less than half the homes that changed hands went for over a million.

Both those numbers are correct. Well, ok,  I assume they are both correct; they are both what they are supposed to be.  It’s just that home sales aren’t a random sample of all homes.  But if the median sale price is $990k and the median valuation for all homes is $1.2m, you can see that interpreting these numbers is harder than it looks.

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, 42 of which were correctly predicted, a success rate of 67.7%.
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.

Team Ratings for Round 8

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 56 matches played, 28 of which were correctly predicted, a success rate of 50%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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.