Posts filed under General (3152)

October 11, 2018

Carefully taught

Q: It’s shocking how computers can be so sexist

A: Not really the computers; more the users

Q: But they took this computer program and showed it lots of people’s applications, and it downrated the ones from women

A: Yes, but that’s because they also trained it with information about which applications they thought were best, and it learned from them that women’s applications weren’t as good

Q: Couldn’t it just have seen that more men that women were accepted because more men applied, and over-generalised?

A: Not really. It should be looking at the probability of acceptance, which wouldn’t be affected by overall proportions, but would be affected by human bias.

Q: Could the bias all have come in via word associations, like in that ‘how to make a racist AI’ blog post.

A: Perhaps. But only if they weren’t really trying. In particular, however the bias came in, they should have been aware of the potential and audited the results. I mean, this is a respectable organisation; you’d assume they were that responsible

Q: That sounds like a simple piece of advice

A: Yes, but even 30 years later, people are still making the same mistakes

Q: Wait, what? Aren’t we talking about Amazon?

A: No, St George’s Hospital Medical School, London.  In the BMJ in 1988, based on a program written in the 1970s

October 9, 2018

Mitre 10 Cup 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
Canterbury 14.38 15.32 -0.90
Wellington 12.22 12.18 0.00
Tasman 8.59 2.62 6.00
Waikato 8.04 -3.24 11.30
North Harbour 6.47 6.42 0.10
Auckland 6.18 -0.50 6.70
Otago -1.22 0.33 -1.50
Counties Manukau -2.77 1.84 -4.60
Taranaki -3.77 6.58 -10.30
Bay of Plenty -4.72 0.27 -5.00
Northland -6.24 -3.45 -2.80
Hawke’s Bay -6.30 -13.00 6.70
Manawatu -11.44 -4.36 -7.10
Southland -21.60 -23.17 1.60

 

Performance So Far

So far there have been 62 matches played, 43 of which were correctly predicted, a success rate of 69.4%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Otago vs. Bay of Plenty Oct 03 45 – 34 8.30 TRUE
2 Wellington vs. Auckland Oct 04 24 – 29 13.30 FALSE
3 Hawke’s Bay vs. Manawatu Oct 05 45 – 17 5.00 TRUE
4 Northland vs. Waikato Oct 06 28 – 71 -3.10 TRUE
5 North Harbour vs. Counties Manukau Oct 06 36 – 26 14.00 TRUE
6 Canterbury vs. Taranaki Oct 06 41 – 7 19.50 TRUE
7 Southland vs. Bay of Plenty Oct 07 22 – 26 -15.10 TRUE
8 Otago vs. Tasman Oct 07 21 – 47 -1.60 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 Southland vs. Auckland Oct 10 Auckland -23.80
2 Tasman vs. Hawke’s Bay Oct 11 Tasman 18.90
3 Taranaki vs. Wellington Oct 12 Wellington -12.00
4 Bay of Plenty vs. Northland Oct 13 Bay of Plenty 5.50
5 Waikato vs. Otago Oct 13 Waikato 13.30
6 Counties Manukau vs. Canterbury Oct 13 Canterbury -13.10
7 Auckland vs. North Harbour Oct 14 Auckland 3.70
8 Manawatu vs. Southland Oct 14 Manawatu 14.20

 

October 5, 2018

Briefly

  • “Data for Sale” at Stuff, on data ethics
  • “How a math genius hacked OkCupid to find true love” at Wired
  • Chris Knox interviews Cathy O’Neil, who is in New Zealand for a Stats NZ ‘Data Summit’.  StatsChat readers will already be familiar with Dr O’Neil aka mathbabe.org
  •  ‘People disagree about what fairness looks like. That’s true in general, and also true when you try to write down a mathematical equation and say, “This is the definition of fairness.”’ An interview with Dr Kristian Lum  of the Human Rights Data Analysis Group
  • A group at Johns Hopkins Dept of Biostatistics have been working to reduce the scarcity value of data science. They have a new program: “excited to announce the first part of our new system, a new set of massive online open courses called Chromebook Data Science. These MOOCs are for anyone from high schoolers on up to get into data science. If you can read and follow instructions you can learn data science from these courses!” (There’s obviously a potential conflict of interest here with Auckland’s data science programs, but I think there’s a separate market for in-person training where you can ask questions)
  • “Which neighborhoods in America offer children the best chance at a better life than their parents? The Opportunity Atlas uses anonymous data following 20 million Americans from childhood to their mid-thirties to answer this question.” There’s an obvious difficulty with any dataset like this — if you’re looking at people in their mid-thirties, they were children quite a while ago and things may have changed.  Still interesting to explore.
October 4, 2018

Australia votes for a shag

It’s time for StatsChat’s favourite bogus poll: Forest & Bird’s Bird of the Year.

In contrast to most bogus online polls, Bird of the Year doesn’t pretend to be anything more than a publicity stunt, and no-one seriously believes the huge year-to-year variation in the results has any real meaning in popular opinion

Bird of the Year still has more quality control than most bogus polls. They require a unique email address per vote, and this year have monitoring by Dragonfly Data Science.

Dragonfly noticed an apparent attempt to hack the vote last night, with a large number of votes from a single Australian IP address for the cormorants or shags, kawau in te reo.

Yes, Bird of the Year is a joke. But any other online clicky poll is at least as much of a joke.

(PS: for the sake of people whose tolerance for this sort of thing is lower than yours, if you tweet about Bird of the Year, use the hashtag)

October 2, 2018

International comparisons

From Pew Research, via Twitter

That list of European countries, presumably intended to give the most meaningful comparison to the USA, is a bit unusual.

It includes Malta and Cyprus and Lichtenstein, but doesn’t include Ireland or the UK.

 

Pharmac rebates

There’s an ‘interactive’ at Stuff about the drug rebates that Pharmac negotiates. The most obvious issue with it is the graphics, for example

and

The first of these is a really dramatic illustration of a well-known way graphs can mislead: using just one dimension of a two-dimensional or three-dimensional thing to represent a number. The 2016/7 capsule looks much more than twice as big as the puny little 2014/15 one, because it’s twice as high and twice as wide (and by implication from shading, twice as deep).  The first graph also commits the accounting sin of displaying a trend from total, nominal expenditures rather than real (ie, inflation-adjusted) per-capita expenditures.

The second one is not as bad, but the descending line to the left of the data points is a bit dodgy, as is the fact that the x-axis is different from the first graph even though the information should all be available.  Also, given that rebates are precisely not a component of Pharmac’s drug spend, the percentage is a bit ambiguous.  The graph shows total rebates divided by what would have been Pharmac’s “drug spend” in the improbable scenario that the same drugs had been bought without rebates. That is, in the most recent year, Pharmac spent $849 million on drugs. If rebates were $400m as shown in the first graph, the percentage in the second graph is something like ($400 million)/($400 million+$849 million)=32%.

More striking when you listen to the whole thing, though,  is how negative it is about New Zealand getting these non-public discounts on expensive drugs.  In particular, the primary issue raised is whether we’re getting better or worse discounts than other countries (which, indeed, we don’t know), rather than whether we’re getting good value for what we pay — which we basically do know, because that’s exactly what Pharmac assesses.  

Now, since the drug companies do want to keep their prices secret there must be some financial advantage to them in doing so, thus there is probably some financial disadvantage to someone other than them.   It’s possible that we’re in that group; that other comparable countries are getting better prices than we are. It’s also possible that we’re getting better prices than them.  Given Pharmac’s relatively small budget and their demonstrated and unusual willingness not to subsidise overpriced new drugs, I know which way I’d guess.

There are two refreshing aspects to the interactive, though.  First, it’s good to see explicit consideration of the fact that drug prices are primarily not a rich-country problem.   Second, it’s good to see something in the NZ mass media in favour of the principle that Pharmac can and should walk away from bad offers. That’s a definite change from most coverage of new miracle drugs and Pharmac.

Mitre 10 Cup 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
Wellington 13.87 12.18 1.70
Canterbury 13.08 15.32 -2.20
North Harbour 6.83 6.42 0.40
Tasman 6.37 2.62 3.80
Auckland 4.53 -0.50 5.00
Waikato 4.45 -3.24 7.70
Otago 0.75 0.33 0.40
Taranaki -2.47 6.58 -9.00
Northland -2.65 -3.45 0.80
Counties Manukau -3.12 1.84 -5.00
Bay of Plenty -3.50 0.27 -3.80
Hawke’s Bay -8.37 -13.00 4.60
Manawatu -9.37 -4.36 -5.00
Southland -22.57 -23.17 0.60

 

Performance So Far

So far there have been 54 matches played, 36 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Hawke’s Bay vs. Northland Sep 26 55 – 41 -4.20 FALSE
2 Bay of Plenty vs. Manawatu Sep 27 15 – 17 12.50 FALSE
3 Auckland vs. Otago Sep 28 26 – 31 10.60 FALSE
4 Waikato vs. Southland Sep 29 42 – 11 31.00 TRUE
5 Taranaki vs. North Harbour Sep 29 26 – 55 -0.10 TRUE
6 Wellington vs. Tasman Sep 29 22 – 28 15.30 FALSE
7 Canterbury vs. Hawke’s Bay Sep 30 49 – 24 27.20 TRUE
8 Counties Manukau vs. Northland Sep 30 20 – 24 3.50 FALSE

 

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 Otago vs. Bay of Plenty Oct 03 Otago 8.30
2 Wellington vs. Auckland Oct 04 Wellington 13.30
3 Hawke’s Bay vs. Manawatu Oct 05 Hawke’s Bay 5.00
4 Northland vs. Waikato Oct 06 Waikato -3.10
5 North Harbour vs. Counties Manukau Oct 06 North Harbour 14.00
6 Canterbury vs. Taranaki Oct 06 Canterbury 19.50
7 Southland vs. Bay of Plenty Oct 07 Bay of Plenty -15.10
8 Otago vs. Tasman Oct 07 Tasman -1.60

 

Currie Cup 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.

Note that Cheetahs2 refers the Cheetahs team when there is a Pro14 match. The assumption is that the team playing in the Pro14 is the top team and the Currie Cup team is essentially a second team.


Current Rating Rating at Season Start Difference
Western Province 7.82 4.66 3.20
Sharks 3.62 4.18 -0.60
Lions 2.27 3.23 -1.00
Cheetahs 2.23 3.86 -1.60
Blue Bulls 0.52 0.94 -0.40
Pumas -7.64 -8.36 0.70
Griquas -10.39 -9.78 -0.60
Cheetahs2 -29.69 -30.00 0.30

 

Performance So Far

So far there have been 18 matches played, 15 of which were correctly predicted, a success rate of 83.3%.
Here are the predictions for last week’s games.


Game Date Score Prediction Correct
1 Western Province vs. Sharks Sep 29 50 – 28 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 Pumas vs. Lions Oct 12 Lions -5.40
2 Griquas vs. Sharks Oct 13 Sharks -9.50
3 Blue Bulls vs. Western Province Oct 13 Western Province -2.80

 

September 27, 2018

Reading clickbait

Q: Did you see women who own horses live 15 longer than those who don’t?

A: Fifteen what?   [thanks, David Hood]

Q: Years.

A:  There’s an obvious reasons why women who own horses would live longer than those who don’t. Horses are expensive; women who can afford them will be more affluent than average. There could easily be other confounding factors, too

Q: But 15 years?!

A: Ok, that’s a lot. But remember, this is just an observational study — and it might not even be a representative sample. It could be some sort of clicky bogus poll

Q: Where they ask people if they own a horse and how old they were when they died? Yeah right.

A: Um. Ok. Maybe not a bogus poll. But 15 years just isn’t plausible, and it’s obviously not a randomised trial.

Q: “The double blind study followed women in different age groups over a forty year time frame to capture this objective data.”

A: Double blind?

Q: What it says.

A: How could you possibly have a double blind study of horse ownership?

Q: They could get alpacas instead. Or virtual reality games about horses. Or something.

A:  That’s not double blind. That’s active-control. Double blind would mean you got an alpaca instead of a horse AND YOU COULDN’T TELL! Is there a link to the research?

Q: I hoped you’d find it, like you usually do.

A:

Q:

A: Really?

Q:

A: Ok. One of the other copies of this story says the lead scientist is Gary Cockburn.  There are three papers on the PubMed database with a “G Cockburn” as author. None is even slightly related to this story.  There are seven papers with a “Cockburn” as author and some reference to “horse”. None is even slightly related to this story. AND YOU CAN’T HAVE A DOUBLE BLIND STUDY OF HORSES!

Q: They made it up?

A:  This is why you shouldn’t follow those links at the bottom of the page

 

Briefly