Posts from July 2012 (55)

July 13, 2012

When randomized trials don’t help

The Herald reports on a study of weight gain after quitting smoking, which is based on analyzing the results of 62 randomized trials of treatments to help quitting.  Ordinarily, data from randomized trials is what we want, so why is Professor Simon Chapman quoted as complaining the results are unreliable?

Well, it’s partly because he doesn’t like anything that might be construed as favorable about smoking, but he has a good point in this case.  Randomized trials give us fair and trustworthy comparisons between two treatments: in this case the 62 trials tell us something about which ways to quit actually lead to the most quitting.   The information on weight gain, on the other hand, isn’t a comparison of two randomized treatments, it’s a before-after comparison on the people who managed to quit.  The fact that the treatments were randomized is of no help at all, since the analysis lumps all quitters together.

In fact, it’s worse than that. A lot of smokers manage to quit with only moderate difficulty.  They tend not to end up in randomized trials.  Some smokers find quitting much harder, and they are much more likely to end up in randomized trials.  So the research actually has found that a group of people who probably found quitting hard have gained 4-5kg after quitting.   It’s quite likely that people who find quitting hard are also going to gain more weight than those who quit without major difficulties, so we may well be overestimating the impact of quitting on weight.

 

Our new robot overlords

Since I regularly complain about the lack of randomised trials in education, I really have to mention a recent US study.  At six public universities in the US, introductory statistics students who consented were randomised between the usual sort of teaching by real live instructors or a format with one hour per week of face-to-face instruction augmented by independent computer-guided instruction.  Within each campus, the students were assessed in the same way regardless of their instruction method, and across all campuses they also took a standardised test of statistics competence.   Statistics is a good target for this sort of experiment, because it is a widely required course, and the median introductory statistics course is not very good.

The results were interesting.  The students using the hybrid computer-guided approach found the course less interesting than those with live instructors, but their performance in the course and in the standardised tests was the same.   If you ignore the cost of developing the software (which in this case already existed), the computer-guided approach would allow more students to be taught by the same number of instructors, saving money in the long run.

This doesn’t mean instructors are obsolete — people like face-to-face classes, and we do actually care if students end up interested in statistics –but it does mean that we need to think about the most efficient ways to use class contact time.  There’s an old joke about lectures as a method of transferring information from the lecturer’s notes into the students’ notebooks without it passing through the brains of either.  We’ve got the internet for that, now.

July 11, 2012

NRL Predictions, Round 19

Team Ratings for Round 19

Here are the team ratings prior to Round 19, 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.

Current Rating Rating at Season Start Difference
Bulldogs 7.08 -1.86 8.90
Sea Eagles 5.47 9.83 -4.40
Storm 5.16 4.63 0.50
Warriors 4.82 5.28 -0.50
Broncos 4.04 5.57 -1.50
Rabbitohs 3.09 0.04 3.00
Cowboys 1.96 -1.32 3.30
Wests Tigers 0.63 4.52 -3.90
Sharks -0.61 -7.97 7.40
Dragons -2.51 4.36 -6.90
Titans -3.68 -11.80 8.10
Raiders -3.70 -8.40 4.70
Knights -4.44 0.77 -5.20
Roosters -4.48 0.25 -4.70
Panthers -8.16 -3.40 -4.80
Eels -8.41 -4.23 -4.20

 

Performance So Far

So far there have been 128 matches played, 76 of which were correctly predicted, a success rate of 59.38%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Wests Tigers vs. Bulldogs Jul 06 20 – 32 -0.04 TRUE
2 Storm vs. Raiders Jul 07 12 – 40 21.23 FALSE
3 Titans vs. Warriors Jul 07 14 – 32 -1.34 TRUE
4 Rabbitohs vs. Knights Jul 08 34 – 14 10.50 TRUE
5 Sea Eagles vs. Eels Jul 08 40 – 24 18.84 TRUE
6 Sharks vs. Roosters Jul 09 14 – 14 9.96 FALSE

 

Predictions for Round 19

Here are the predictions for Round 19

Game Date Winner Prediction
1 Bulldogs vs. Eels Jul 13 Bulldogs 20.00
2 Broncos vs. Warriors Jul 13 Broncos 3.70
3 Knights vs. Sea Eagles Jul 14 Sea Eagles -5.40
4 Storm vs. Cowboys Jul 14 Storm 7.70
5 Wests Tigers vs. Panthers Jul 14 Wests Tigers 13.30
6 Raiders vs. Titans Jul 15 Raiders 4.50
7 Dragons vs. Sharks Jul 15 Dragons 2.60
8 Roosters vs. Rabbitohs Jul 16 Rabbitohs -3.10

 

Super 15 Predictions, Week 21

Team Ratings for Week 21

Here are the team ratings prior to Week 21, 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.

Current Rating Rating at Season Start Difference
Crusaders 9.85 10.46 -0.60
Stormers 5.99 6.59 -0.60
Sharks 4.23 0.87 3.40
Hurricanes 3.57 -1.90 5.50
Bulls 3.28 4.16 -0.90
Chiefs 2.91 -1.17 4.10
Brumbies 1.05 -6.66 7.70
Reds 0.90 5.03 -4.10
Waratahs -2.49 4.98 -7.50
Highlanders -3.17 -5.69 2.50
Cheetahs -3.44 -1.46 -2.00
Blues -4.71 2.87 -7.60
Lions -8.71 -10.82 2.10
Force -10.01 -4.95 -5.10
Rebels -12.56 -15.64 3.10

 

Performance So Far

So far there have been 113 matches played, 80 of which were correctly predicted, a success rate of 70.8%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Chiefs vs. Crusaders Jul 06 21 – 28 -1.60 TRUE
2 Reds vs. Highlanders Jul 06 19 – 13 9.10 TRUE
3 Sharks vs. Bulls Jul 06 32 – 10 2.30 TRUE
4 Blues vs. Force Jul 07 32 – 9 7.30 TRUE
5 Waratahs vs. Brumbies Jul 07 15 – 19 1.90 FALSE
6 Cheetahs vs. Stormers Jul 07 6 – 13 -4.50 TRUE
7 Lions vs. Rebels Jul 07 37 – 32 9.00 TRUE

 

Predictions for Week 21

Here are the predictions for Week 21. The prediction is my estimated 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. Chiefs Jul 13 Hurricanes 5.20
2 Brumbies vs. Blues Jul 14 Brumbies 10.30
3 Crusaders vs. Force Jul 14 Crusaders 24.40
4 Reds vs. Waratahs Jul 14 Reds 7.90
5 Stormers vs. Rebels Jul 14 Stormers 23.10
6 Sharks vs. Cheetahs Jul 14 Sharks 12.20
7 Bulls vs. Lions Jul 14 Bulls 16.50

 

July 9, 2012

Earthquake maps

Stuff is linking to a map of earthquakes by John Nelson of IDV Solutions.  Long-term readers may recall my earthquake map, which uses just the earthquakes since 1973, where the data is more complete.   John Nelson’s map is certainly prettier, but I think mine is clearer.

Book review: Thinking, Fast and Slow

Daniel Kahneman and Amos Tversky made huge contributions to our understanding of why we are so bad at prediction.  Kahneman won a Nobel Prize[*] for this in 2002 (Tversky failed to satisfy the secondary requirement of still being alive).  Kahneman has now written a book, Thinking, Fast and Slow about their research.  Unlike some of his previous writing, this book is designed to be shelved in the Business/Management section of bookshops and read by people who might otherwise be  looking for their cheese.

The “Fast” and “Slow” of the title are two systems of thought: the rapid preconscious judgement that we use for most of our decision-making, and the conscious and deliberate evaluation of alternatives and probabilities that we like to believe we use.   The “Fast” system relies very heavily on stereotyping — finding the best match for a situation in a library of stories — and so is subject to predictable and exploitable biases.  The “Slow” system can be trained to do much better, but only if we can force it to be used.

A dramatic example of the sort of mischief the “fast” system can get up to is anchoring bias.  Suppose you ask a bunch of people how many UN-member countries are in Africa.  You will get a range of guesses, probably not very accurate, and perhaps a few people who actually know the answer.  Suppose you had first asked people to write down the last two digits of their telephone number, or to spin a roulette wheel and write down the number that is chosen, and then to guess how many countries there are in Africa.  Empirically, across a range of situations like this, there is a strong correlation between the obviously irrelevant first number and the guess.   This is an outrageous finding, but it is very well confirmed.   It’s one of the reasons that bogus polls are harmful even if you know they are bogus.

Kahneman gives many other examples of cognitive illusions generated by the ‘fast’ system of the mind.  As with optical illusions, they don’t lose their intuitive force when you understand them, but you can learn not to trust your intuition in situations where it’s going to be biased.

One minor omission of the book is that there’s not much explanation of why we are so stupid: Kahneman points out, and documents, that thinking uses up blood sugar and is biologically expensive, but that doesn’t explain why the mistakes we make are so simple.  Research in computer science and philosophy, by people actually trying to implement thinking, gives one possibility, under the general name of “the frame problem“.  We know an enormous number of facts and relationships between them, and we cannot afford to investigate the logical consequences of all these facts when trying to make a decision.  The price of tea in China really is irrelevant to most decisions, but not to decisions about tea purchases, or about souvenir purchases when in Beijing, or to living-wage levels in Fujian.  We need some way of ignoring the price of tea in China, and millions of other facts, except very occasionally when they are relevant, without having to deduce their irrelevance each time.  Not surprisingly, it sometimes misfires and treats information as important when it is actually irrelevant.

Read this book.  It might help you think better, and at least will give you better excuses for your mistakes.

 

* to quote Daniel Davies: “blah blah blah Sveriges Riksbank. Nobody cares, you know.”

Stat of the Week Winner: June 30 – July 6 2012

Congratulations to Tony Cooper for his nomination of “Men over 50 nation’s biggest drinkers”, which Thomas Lumley picked up on and posted about as a result.

It’s a great example of how the media can get it so wrong – and in doing so has created a myth. Congratulations Tony!

NBR calls Stats Chat a “brilliant daily skewing of journalistic bloopers”

NBR’s editor Nevil Gibson links up to Stats Chat today in his discussion of the minimum price of alcohol:

In StatsChat’s brilliant daily skewing of journalistic bloopers, University of Auckland Biostatistics Professor Thomas Lumley says this is the opposite point of minimum unit pricing, as opposed to increased excise rates.

Read the full editorial »

Stat of the Week Competition: July 7 – 13 2012

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 July 13 2012.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of July 7 – 13 2012 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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Stat of the Week Competition Discussion: July 7 – 13 2012

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!