Stats Chat

There’s a website that shows a map of all the buildings in the Netherlands, colour-coded by age. To start you off, here’s central Amsterdam

(from Abi Sutherland)

Two examples from other blogs

Mark Liberman, in two posts at Language Log, tackles the claim

Perhaps unsurprisingly, the paper, “Cluttered Writing: Adjectives and Adverbs in academia,” finds that social science papers contain the highest density, followed by humanities and history. Natural science and mathematics contain the lowest frequency, followed by medicine and business and economics.

The difference between the social and the natural sciences is about 15 percent. “Is there a reason that a social scientist cannot write as clearly as a natural scientist?” the paper asks.

He shows that the paper doesn’t provide reliable evidence for the claims, and, in the second post, that neither the claim that social scientists use more modifiers, nor the claim that clear writing uses fewer modifiers is supported by data.

Frances Woolley, at Worthwhile Canadian Initiative, uses life tables to examine the question of grandparents’ funerals as a student excuse. She finds

It turns out, given the age and gender distribution assumed above, and Canadian mortality rates, the odds of all 200 grandparents surviving the term is just 16 percent. In large undergraduate classes, some grandparents will pass away almost every semester.

and

I think some undergraduate students don’t realize just how public and well documented deaths are. It’s far easier to verify whether or not a grandmother died than it is to know whether or not a student was genuinely too sick to complete an assignment. 

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 September 13 2013.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of September 7 – 13 2013 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.

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One of the many quotes attributed to film producer Samuel Goldwyn is

Any man who goes to a psychiatrist ought to have his head examined.

This neatly summarises what epidemiologists call ‘confounding by indication’, that is, the fact that treatments tend to look harmful just because they are only given to sick people.  For example, in a study I worked on in Seattle, elderly people who were taking blood-pressure medication were about 20% more likely to have heart attackes than those not taking blood-pressure medication, although we know from randomised trials that these blood-pressure medications actually reduce heart attacks by about 20%.

The impact can easily be much more dramatic: about 12% of people receiving a heart transplant die within a year, compared to 0.6% of all Kiwis, a 20-fold higher rate, and the HIV deniers have made much of the fact that nearly  all AIDS deaths in Western countries are in people who have received antiretroviral treatment for HIV infection.

In the Herald, Rodney Hide doesn’t seem to appreciate the power of confounding by indication.

The benefit-supported children were six times more likely to be abused than those who were not benefit-supported. And they were 14 times more likely to be known to Youth Justice.

Those in households benefit-dependent for nine or more years were 13 times more likely to be abused and 29 times more likely to be known to Youth Justice.

He concedes that the numbers don’t prove that the benefit support is the cause, and describes some of the factors that might lead to confounding by indication, but says

Nonetheless, the ministry factsheet is suggestive. If the benefit system were a commercial product the Government would demand a warning: Danger: Taking a benefit can endanger your children.

and describes the Ministry’s

“These findings are consistent with associations between low income and measures of child maltreatment found both across and within countries. They do not, however, establish that being supported by the benefit system causes a child to be more at risk of these outcomes.”

as doublespeak.  But in fact, the Ministry is quite right.  Comparing people who need and qualify for benefits to the rest of the country isn’t even suggestive, any more than a comparison of heart transplant recipients or people taking antiretroviral drugs to the rest of us.  And that’s without even considering the ‘PC’ issues such as whether abusing your kids might be harder to hide if you’re on benefit than if you’re rich.

Readers of history or classic literature will recall that poor children didn’t fare all that well before benefits were introducted, and a brief look at the UNICEF web site will confirm that today children can be much worse off in countries where there isn’t a functioning government benefit system.  Is that “suggestive” too?

From the New York Times:

Statistical analysis has swept through baseball over the past decade, becoming part of the fabric of the game and an object of growing fascination to its fans. As players, managers and front office executives embrace the esoteric statistics, teams increasingly want their radio announcers just as fluent in the language of WAR, VORP and B.A.B.I.P.

Say what?

Read the full story here.

Team Ratings for Round 5

Here are the team ratings prior to Round 5, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

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 12 matches played, 5 of which were correctly predicted, a success rate of 41.7%.

Here are the predictions for last week’s games.

Predictions for Round 5

Here are the predictions for Round 5. 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 26

Here are the team ratings prior to Round 26, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 184 matches played, 112 of which were correctly predicted, a success rate of 60.87%.

Here are the predictions for last week’s games.

Predictions for Round 26

Here are the predictions for Round 26. 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.

In the run up to the US election, there was a popular factoid to the effect that  50% of the US population paid no tax. This was, of course, untrue, but it could be arrived at by minor blurring of a true statement:  47% of tax units (family or individual) paid no net federal income tax.   The US has a strongly progressive federal income tax (more so than NZ), combined with a moderately regressive federal payroll tax and state and local taxes.  The average tax rate ranges from 17% for the poorest 20% of people, to 25% for the middle 20%, to 29% for the richest 1%. These figures are potentially misleading in the other direction, since they don’t count benefits, but the US benefit system is relatively limited and the figures for the middle 20% and up would not be appreciably different.

Doing this sort of calculation precisely is surprisingly difficult, because it’s not as easy as you might think to work out who really pays which taxes.  For example, you might assume that shoppers pay GST and land owners pay land taxes/rates, but in fact renters end up paying a chunk of the rates in increased rent, and retailers pay a small part of GST in reduced profits.   In New Zealand it’s probably a reasonable approximation to just count income tax and GST, and to assume these are effectively paid by earners and buyers.  It’s not a reasonable approximation to just count income tax — and publishing estimates of tax share that only use income tax and benefits, even if you do it with scrupulous accuracy, is likely to lead to the sort of misquotation that we saw in the US.

Over at pundit.co.nz, Rob Salmond does some calculations, and while I think the tone is unnecessarily inflammatory, the final numbers look plausible. The 10% highest-income households  get about 30% of the income and they (actually, ‘we’, I think) pay 43% of the net tax.  [Rob also mentions the highest-wealth 10% of families, implying incorrectly that these are the same people].

For comparison, in the US, it’s the highest-income 5% of the population that gets 35% of the income and they pay 37% of the tax. Since benefits are about 28% of US federal spending, they are about 18% of all taxes, and so the share of net taxes for the top 5% will be about 44%. NZ is both less unequal and has a slightly more progressive tax and transfer system (and we have a national health system, too).  In the other direction, we have more inequality and a less progressive tax and transfer system than, say, Sweden.

If we stipulate that a mix of income tax and GST, with few deductions, is what we’re working with, making the  tax system more progressive has obvious benefits in terms of reducing inequality, and costs in terms of reducing productivity.  Making this tradeoff is a policy issue rather than a technical one, and it’s perfectly reasonable for people to argue for their own positions as strongly as they can: inequality and productivity are worth arguing about.

On the other hand, if you’re going to use a relatively unnatural statistic based on income tax and benefits but not GST, aggregated in an unusual way, I think you have the responsibility to point out carefully  and explicitly that you’ve left out GST (and, ideally, explain why). Bill English said ” The 1.3 million households with incomes under $110,000 a year collectively pay no net tax—that is, their total income support payments match their combined income tax.” The clause before the dash is simply false. The clause after the dash is true, but I don’t think it’s a contribution to informed public debate.

Two links,

Deciding who to interview: Aline Lerner looked at resumes of 300 candidates interviewed at a Silicon Valley company to see what predicted getting the job. The biggest factor wasn’t grades or degree or experience, it was typos  — and this was among people who got an interview.

Did it work? An interview with a Google exec by the New York Times

We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert.

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 September 6 2013.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of August 31 – September 6 2013 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.

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