April 25, 2017

Electioneering and statistics

In New Zealand, the Government Statistician reports to the Minister of Statistics, currently Mark Mitchell.  For about a decade, the UK has had a different system, where the National Statistician reports to the UK Statistics Authority, which is responsible directly to Parliament. The system is intended to make official statistics more clearly independent of the government of the day.

An additional role of the UK Statistics Authority is as a sort of statistics ombudsman when official statistics are misused.  There’s a new letter from the Chair to the UK political parties

The UK Statistics Authority has the statutory objective to promote and safeguard the production and publication of official statistics that serve the public good.

My predecessors Sir Michael Scholar and Sir Andrew Dilnot have in the past been obliged to write publicly about the misuse of official statistics in other pre-election periods and during the EU referendum campaign. Misuse at any time damages the integrity of statistics, causes confusion and undermines trust.

I write now to ask for your support and leadership to ensure that official statistics are used throughout this General Election period and beyond, in the public interest and in accordance with the principles of the Code of Practice for Official Statistics. In particular, the statistical sources should be clear and accessible to all; any caveats or limitations in the statistics should be respected; and campaigns should not pick out single numbers that differ from the picture painted by the statistics as a whole.

I am sending identical letters to the leaders of the main political parties, with a copy to Sir Jeremy Heywood, Cabinet Secretary.

We don’t have anyone whose job it is to write that sort of letter here, but it would be nice if the political parties (and their partisans) still followed this advice.

Super 18 Predictions for Round 10

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.

Current Rating Rating at Season Start Difference
Hurricanes 17.51 13.22 4.30
Crusaders 11.67 8.75 2.90
Chiefs 9.68 9.75 -0.10
Highlanders 7.88 9.17 -1.30
Lions 7.29 7.64 -0.30
Brumbies 2.63 3.83 -1.20
Stormers 2.53 1.51 1.00
Blues 2.40 -1.07 3.50
Sharks 0.04 0.42 -0.40
Waratahs -0.23 5.81 -6.00
Jaguares -1.46 -4.36 2.90
Bulls -2.87 0.29 -3.20
Force -8.40 -9.45 1.10
Cheetahs -9.61 -7.36 -2.20
Reds -10.42 -10.28 -0.10
Rebels -10.75 -8.17 -2.60
Kings -15.90 -19.02 3.10
Sunwolves -19.10 -17.76 -1.30

 

Performance So Far

So far there have been 71 matches played, 55 of which were correctly predicted, a success rate of 77.5%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Hurricanes vs. Brumbies Apr 21 56 – 21 16.70 TRUE
2 Lions vs. Jaguares Apr 21 24 – 21 14.10 TRUE
3 Highlanders vs. Sunwolves Apr 22 40 – 15 31.80 TRUE
4 Crusaders vs. Stormers Apr 22 57 – 24 10.40 TRUE
5 Waratahs vs. Kings Apr 22 24 – 26 22.60 FALSE
6 Force vs. Chiefs Apr 22 7 – 16 -14.80 TRUE
7 Bulls vs. Cheetahs Apr 22 20 – 14 10.80 TRUE
8 Sharks vs. Rebels Apr 22 9 – 9 16.80 FALSE

 

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.

Game Date Winner Prediction
1 Highlanders vs. Stormers Apr 28 Highlanders 9.40
2 Chiefs vs. Sunwolves Apr 29 Chiefs 32.80
3 Reds vs. Waratahs Apr 29 Waratahs -6.70
4 Force vs. Lions Apr 29 Lions -11.70
5 Cheetahs vs. Crusaders Apr 29 Crusaders -17.30
6 Kings vs. Rebels Apr 29 Rebels -1.10
7 Jaguares vs. Sharks Apr 29 Jaguares 2.50
8 Brumbies vs. Blues Apr 30 Brumbies 4.20

 

April 24, 2017

Briefly

  • The Herald (from the Daily Mail) recommends drinking beetroot juice, based on a study of brain waves: “This finding could help people who are at-risk of brain deterioration to remain functionally independent, such as those with a family history of dementia“.  The NHS Choices blog commented on a similar study by the same research group in 2010; their comments still apply.
  • Testimonials and motivational speakers tell you “I did this and look how it turned out”.  As XKCD illustrates, results may not be typical 
  • “Data made available for reanalysis, a journal that promptly responded to the outcomes of that reanalysis, and a finding that could save lives.” (from Stat). Another moral to the story: don’t edit data by copy-and-paste.
  • The company says it has studies that back up its claims, but refused to release them on the grounds that they are commercial-in-confidence.” It appears that Johnson & Johnson would rather pull their ad than let people look at the evidence. (from The Age)
  • it’s not acceptable if you’ve got the information readily available to leave it to the last minute for release, that’s not what the Act says you can do”  The Chief Ombudsman interviewed by Newsroom  about the Official Information Act.

And finally

If you give a mouse a strawberry…

 

So, the Herald (from the Daily Mailhas a headline Why women should eat a punnet of strawberries a day. That seems a little extreme, especially as punnets of strawberries are fairly seasonal.

The story leads off with

Eating just 15 strawberries a day protected mice from aggressive breast cancer in a new medical study.

So, first of all, mice, not women.  Also, when you go to the open-access research paper, it didn’t exactly ‘protect’ the mice.  The mice had cells from a breast cancer cell culture implanted under their skins, and the study looked at the change in size of those implanted tumours, not at spread within the mouse or health of the mouse or anything like that.  It’s a useful approach to learning about cancer cell biology, but not all that close to preventing or treating human cancers.

More surprisingly, though, “15 strawberries a day” seems quite a lot for a mouse — several times its body weight. The story changes a bit later:

In total, the strawberries made up 15 percent of the mice’s diet. That is just shy of the recommended daily amount of fruit we should eat each day, and would be equivalent to a punnet of strawberries, reported the Daily Mail.

A figure of 15% seems more plausible than 15 strawberries, though it’s still not quite true, since actually the mice were given concentrated strawberry extract in their food rather than strawberries.  Using the standard (lowish) estimate of 2000 kcal/day, 15% of calories would  be 300 kcal/day  which would take nearly a kilogram of strawberries.

Previous studies have already shown that eating between 10 and 15 strawberries a day can make arteries healthier by reducing blood cholesterol levels.

There isn’t a reference, but the same researcher has studied strawberries and cholesterol (this time even in humans). The ‘between 10 and 15 strawberries a day’ was actually 500g per day.

[via Sam Warburton]

Stat of the Week Competition: April 22 – 28 2017

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 28 2017.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of April 22 – 28 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…)

April 17, 2017

NRL Predictions for Round 8

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.

Current Rating Rating at Season Start Difference
Raiders 9.43 9.94 -0.50
Storm 8.41 8.49 -0.10
Sharks 8.11 5.84 2.30
Broncos 5.26 4.36 0.90
Cowboys 2.54 6.90 -4.40
Panthers 1.56 6.08 -4.50
Dragons 0.97 -7.74 8.70
Roosters 0.50 -1.17 1.70
Bulldogs -1.74 -1.34 -0.40
Sea Eagles -1.85 -2.98 1.10
Eels -2.15 -0.81 -1.30
Rabbitohs -3.33 -1.82 -1.50
Titans -3.83 -0.98 -2.90
Warriors -5.60 -6.02 0.40
Wests Tigers -5.66 -3.89 -1.80
Knights -14.66 -16.94 2.30

 

Performance So Far

So far there have been 56 matches played, 32 of which were correctly predicted, a success rate of 57.1%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Bulldogs vs. Rabbitohs Apr 14 24 – 9 3.20 TRUE
2 Knights vs. Roosters Apr 14 6 – 24 -10.40 TRUE
3 Broncos vs. Titans Apr 14 24 – 22 14.60 TRUE
4 Sea Eagles vs. Storm Apr 15 26 – 36 -6.10 TRUE
5 Raiders vs. Warriors Apr 15 20 – 8 20.40 TRUE
6 Dragons vs. Cowboys Apr 15 28 – 22 1.00 TRUE
7 Panthers vs. Sharks Apr 16 2 – 28 1.10 FALSE
8 Eels vs. Wests Tigers Apr 17 26 – 22 7.70 TRUE

 

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.

Game Date Winner Prediction
1 Raiders vs. Sea Eagles Apr 21 Raiders 14.80
2 Rabbitohs vs. Broncos Apr 21 Broncos -5.10
3 Eels vs. Panthers Apr 22 Panthers -0.20
4 Cowboys vs. Knights Apr 22 Cowboys 20.70
5 Sharks vs. Titans Apr 22 Sharks 15.40
6 Wests Tigers vs. Bulldogs Apr 23 Bulldogs -0.40
7 Roosters vs. Dragons Apr 25 Roosters 3.00
8 Storm vs. Warriors Apr 25 Storm 18.00

 

Super 18 Predictions for Round 9

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.

Current Rating Rating at Season Start Difference
Hurricanes 16.41 13.22 3.20
Crusaders 10.32 8.75 1.60
Chiefs 10.03 9.75 0.30
Highlanders 8.29 9.17 -0.90
Lions 7.95 7.64 0.30
Stormers 3.88 1.51 2.40
Brumbies 3.73 3.83 -0.10
Blues 2.40 -1.07 3.50
Waratahs 1.24 5.81 -4.60
Sharks 1.05 0.42 0.60
Jaguares -2.12 -4.36 2.20
Bulls -2.58 0.29 -2.90
Force -8.75 -9.45 0.70
Cheetahs -9.90 -7.36 -2.50
Reds -10.42 -10.28 -0.10
Rebels -11.76 -8.17 -3.60
Kings -17.38 -19.02 1.60
Sunwolves -19.50 -17.76 -1.70

 

Performance So Far

So far there have been 63 matches played, 49 of which were correctly predicted, a success rate of 77.8%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Crusaders vs. Sunwolves Apr 14 50 – 3 32.00 TRUE
2 Reds vs. Kings Apr 15 47 – 34 10.70 TRUE
3 Blues vs. Hurricanes Apr 15 24 – 28 -11.40 TRUE
4 Rebels vs. Brumbies Apr 15 19 – 17 -13.90 FALSE
5 Cheetahs vs. Chiefs Apr 15 27 – 41 -16.20 TRUE
6 Stormers vs. Lions Apr 15 16 – 29 1.10 FALSE
7 Bulls vs. Jaguares Apr 15 26 – 13 2.30 TRUE

 

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.

Game Date Winner Prediction
1 Hurricanes vs. Brumbies Apr 21 Hurricanes 16.70
2 Lions vs. Jaguares Apr 21 Lions 14.10
3 Highlanders vs. Sunwolves Apr 22 Highlanders 31.80
4 Crusaders vs. Stormers Apr 22 Crusaders 10.40
5 Waratahs vs. Kings Apr 22 Waratahs 22.60
6 Force vs. Chiefs Apr 22 Chiefs -14.80
7 Bulls vs. Cheetahs Apr 22 Bulls 10.80
8 Sharks vs. Rebels Apr 22 Sharks 16.80

 

Stat of the Week Competition: April 15 – 21 2017

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 21 2017.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of April 15 – 21 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…)

Slow on the uptake

Q: Did you see gin can increase your metabolism?

A: Um…

Q: Here, in the Herald, new research from Latvia!

A:  Not really convincing.

Q: Why? Is it in mice?

A: Up to a point.

Q: <reads> Yes, it’s in mice: “In fact, the mice who were fed regular doses of the spirit saw a 17 percent increase in their metabolic rate”.  That’s a lot, isn’t it?

A: Indeed. One might almost say an incredible amount.

Q:  Ok, were these some special sort of mutant mouse with a weird metabolism?

A: The story doesn’t seem to say.

Q: Of course it doesn’t, but can’t you find the original research paper? The story says it’s in Food & Nature. Doesn’t University of Auckland subscribe to it?

A: No.

Q: That’s usually a bad sign, isn’t it?

A: Especially in this case. The journal doesn’t exist, the university doesn’t exist, and Professor Thisa Lye is, apparently a lie.

Q:  😕?

A: The story is two weeks old. It was an April Fool’s hoax. Thanks to Elle Hunt I was saved potentially quite a bit of time looking for the journal. She tweeted a link from Latvian Public Broadcasting, who have tracked the story down.

Q: So the Herald got it from the Daily Mail who got it from Yahoo who got it from Prima. And none of them checked that the research existed? I mean, ok, checking science isn’t what journalists are trained to do, but checking that sources actually exist? With Google?

A:  On the positive side, no mice were harmed in conducting the research.

April 14, 2017

Cyclone uncertainty

Cyclone Cook ended up a bit east of where it was expected, and so Auckland had very little damage.  That’s obviously a good thing for Auckland, but it would be even better if we’d had no actual cyclone and no forecast cyclone.  Whether the precautions Auckland took were necessary (at the time) or a waste  depends on how much uncertainty there was at the time, which is something we didn’t get a good idea of.

In the southeastern USA, where they get a lot of tropical storms, there’s more need for forecasters to communicate uncertainty and also more opportunity for the public to get to understand what the forecasters mean.  There’s scientific research into getting better forecasts, but also into explaining them better. Here’s a good article at Scientific American

Here’s an example (research page):

hurricane

On the left is the ‘cone’ graphic currently used by the National Hurricane Center. The idea is that the current forecast puts the eye of the hurricane on the black line, but it could reasonably be anywhere in the cone. It’s like the little blue GPS uncertainty circles for maps on your phone — except that it also could give the impression of the storm growing in size.  On the right is a new proposal, where the blue lines show a random sample of possible hurricane tracks taking the uncertainty into account — but not giving any idea of the area of damage around each track.

There’s also uncertainty in the predicted rainfall.  NIWA gave us maps of the current best-guess predictions, but no idea of uncertainty.  The US National Weather Service has a new experimental idea: instead of giving maps of the best-guess amount, give maps of the lower and upper estimates, titled: “Expect at least this much” and “Potential for this much”.

In New Zealand, uncertainty in rainfall amount would be a good place to start, since it’s relevant a lot more often than cyclone tracks.

Update: I’m told that the Met Service do produce cyclone track forecasts with uncertainty, so we need to get better at using them.  It’s still likely more useful to experiment with rainfall uncertainty displays, since we get heavy rain a lot more often than cyclones.