Posts filed under General (3156)

• NPR’s Science Friday covers BAHfest, a competition to produce what look like scientific arguments for nutty conclusions. Hilarious, but also important: serious and scammy pseudoscience uses the same tricks.
• Emma Pierson, a scientist who studies dating analyses a year of emails with her boyfriend
  Him: You’re going to find some weird pattern and break up with me.

  Me: Either that will be warranted by the data, in which case it’s a good thing, or it won’t, in which case I’m a bad statistician. Are you saying I’m a bad statistician?

• And a post by Emma Pierson at 538.com: “people just want to date themselves”

• Another story about changes in cancer risk that just uses number of diagnoses, without even gesturing in the direction of screening bias.

So, I was on a plane from Sydney yesterday that was old enough they told us to switch off our books half an hour before landing. As a result, I actually looked at the Auckland information on the flight map channel (photo taken after we were allowed technology, naturally):

It’s interesting to see where these numbers come from, given all the different ways these things can be defined. Two of these numbers are inconsistent with each other and somewhat obsolete, and the third isn’t even wrong.

According to the Google, the population number 1,377,200 is the June 2011 estimate of the urban population of the Auckland metropolitan area.  Ok, that’s a bit old but so was the plane. Slightly more strange is that StatsNZ thinks the urban Auckland population at 30 June 2011 was 1,351,200, but that’s probably a matter of projections being made in advance and then adjusted as more information comes in. The current (June 2014) estimate is 1,413,500.

So if the population is urban Auckland, what’s the area? With a bit of searching, you can find it’s the area of the old Auckland City, the central Auckland isthmus plus various islands. Auckland City had a population of 450,000 when it was absorbed into the Supercity in 2010. The population and area numbers are for very different entities, and the population number, although old, dates from after the area number became completely obsolete.

The area that goes with the 1,377,200 number is 1,102.9km², the size of the Statistical Urban Area. You could reasonably want the urbanised area (483km²) or the Metropolitan Urban Limits (560km²) as better summaries of the size of Auckland, but they don’t match the quoted population.

That leaves elevation. The picture next to the statistics shows that 78m is not a completely satisfactory characterisation of the elevation of Auckland. The blue stuff with boats floating on it is at sea level (up to tidal variation). Here’s a map (from FloodMap.net) of Auckland elevation; the change from pink to red is at 75m.

Overall, population and area, which could have multiple satisfactory definitions, are defined incompatibly with each other.  Elevation doesn’t really have a satisfactory definition, but isn’t 78m.

Team Ratings for Round 5

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 32 matches played, 19 of which were correctly predicted, a success rate of 59.4%.

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 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 47 matches played, 31 of which were correctly predicted, a success rate of 66%.

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.

The Herald has pictures of the most beautiful faces in the world

and NPR reports on a computer algorithm that can tell if you sound trustworthy or calming or engaging.

The Herald story at least admits these faces are only world-famous in New Zealand (or, rather, the UK)

“It’s important to note that these are the idealised faces according to those living in the UK, so a study in Asia or Africa for example would no doubt have different results.”

The NPR story instead doubles down by saying

But algorithms have stamina, and they do not factor in things like age, race, gender or sexual orientation.

There’s a sense in which this is true, but it’s not a very useful sense. If we can guess age, race, or gender from the sound of someone’s voice, and these perceptions affect whether we think the voice is engaging,calming or trustworthy, our prejudices will show up in the training data and any competent black-box algorithm will learn them.

• Two data-related notes about the Northland by-election: the polls were amazingly accurate given how hard by-elections are to predict, and the Electoral Commission did a wonderful job in getting the vote counted and reported fast.

• A graphic of gender inequality around the world.  The income graph is not the usual one, because it is total income, not income per hour worked — most of the gap is in fewer hours of paid employment.

• According to the Independent “California is in one of its worst-ever droughts because people are growing too much weed”. Unsurprisingly, not what the research actually says

• The Medical Council of New Zealand has released a Discussion Paper on the value of performance and outcome data.

• From Significance magazine, a description of how the various political polling companies in the UK work

From the BBC, Hans Rosling on the Ebola epidemic

(That’s a diagram of the data collection system behind him)

(via Harkanwal Singh)

Q: Did you see that eating a bowl of quinoa every day helps you live longer?

A: No.

Q: There’s story on Stuff (well, from the West Island branches). Is it true?

A: Hard to say.

Q: Well, does the research claim it’s true?

A: Hard to say.

Q: Why? Didn’t they link?

A: No, they linked, and the paper is even open-access. It just doesn’t say anything about the effects of quinoa.

Q: But the story said “A new study by Harvard Public School of Health has found that eating a daily bowl of the protein-packed, gluten-free grain significantly reduces the risk of premature death from cancer, heart disease, respiratory disease and diabetes.”

A: Sadly, yes.

Q: This is your correlation and causation thing again, isn’t it?

A: No, the paper just doesn’t mention quinoa. It talks about grains and cereals.

Q: Ok. So they just didn’t break out the data for quinoa separately. It’s still a grain and a cereal, isn’t it?

A: Yes, as long as you aren’t even more pedantic than me. But it’s not just data analysis. They didn’t even ask their study participants about eating quinoa.

Q: So? Some of the grain they ate must have been quinoa, and there’s no reason to expect it’s different from other grains, is there? Won’t it all get averaged in somehow?

A: I suppose so. But there can’t have been that much of it getting “averaged in”

Q: Why not? You old folks may not have caught on, but quinoa’s getting popular now.

A: The study was in people over 50. That’s older than both of us. Even assuming we weren’t the same person.

Q: Even so. Things are changing. People have more adventurous diets. It’s not the twentieth century any more.

A: It is in the study.

Q: Huh?

A: The dietary data were collected in 1995 and 1997, from people with average age 61 years.

Q: Oh.

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 24 matches played, 16 of which were correctly predicted, a success rate of 66.7%.

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 7

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 40 matches played, 26 of which were correctly predicted, a success rate of 65%.

Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. 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.