Skip to content
January 17, 2008 | 8 Comments

Statisticians global warming plea: don’t forget about us!

Who doesn’t love to read about statistics and statisticians? That’s a rhetorical question, my friends, so don’t bother answering. But I will allude to an answer, by telling you that I begin the statistics classes that I teach by asking the students whether they’d like to learn a magic trick. They always say yes. It goes like this:

Next time you are at a social gathering and somebody introduces themselves to you and asks what you do, say these magic words, “I am a statistician.” And…Poof! They will vanish before your eyes! It never fails.

So it’s not surprising that some of us feel left out from time to time. Which explains why the American Statistical Association (of which I am a member) has issued this statement “endorsing” the conclusions of the IPCC report while also admonishing climatologists to include more statisticians in their work.

The ASA recently convened a meeting of statisticians to ask them how they can be more involved with climate change. The statement was their answer. These sort of meetings do not always go well. The ASA had another such confab back in ’95 and invited Chicago high school students to listen to the delights that awaited them if they chose a career in statistics. The lecture was by the distinguished ASA president, who was thorough, as all statisticians are. At the end of his talk, he opened the floor for questions. There was a period of silence when, finally, one brave young man shouted out, “Yeah. Why are you so goofy?” So you can see the danger.

Anyway, except for the blanket political* “endorsement”, given only to show that we’re willing to play along, the rest of the statement is pretty good, including this, “Over the course of four [IPCC] assessment reports, a small number of statisticians have served as authors or reviewers. Although this involvement is encouraging, it does not represent the full range of statistical expertise available.”

And this, “Even in the satellite era ? the best observed period in Earth?s climate history ? there are significant uncertainties in key observational datasets. Reduction of these uncertainties will be crucial for evaluating and better constraining climate models.”

Most importantly, this, “The design and analysis of computer experiments is an area of statistics that is appropriate for aiding the development and use of climate models. Statistically based experimental designs, not currently used in this field, could be more powerful. It is also important to understand how to combine the results of experiments performed with different climate models. Despite their sophistication, climate models remain approximations of a very complex system and systematic model errors must be identified and characterized.”

The main thrust is that climate scientists have not done as well as they could quantifying the uncertainty in their models, results, and speculations, and that statisticians should be more frequently consulted, because if we’re good at anything, this is it.

We’re also not too bad at magic.

*Of course it’s political, because you cannot simultaneously have a plea for statistical? analysis of climate models while at the same time concluding those analyses are proper.
January 15, 2008 | 7 Comments

Ralph Peters gets his stats right: the New York Times purposely misleads

I’m a veteran and haven’t killed anybody in years. But if you read the New York Times you’d be right to worry that I might.

The Sunday, 13 January 2008, edition of the Times spent four pages! detailing that, in the four and three-quarter years since the Iraq war began, returning soldiers, sailors, and airmen came home horribly scared—mentally, of course—and committed 121 murders. Which is a big number, no question; and probably some, or even most, of the people killed didn’t even have it coming to them.

Military writer Ralph Peters, in today’s column for the New York Post, shows that about 350,000 soldiers have come back from both the Iraqi and Afghanistani wars. That makes the per-year murder rate equal to about 7.3 per 100,000.

Time to seriously fret about the mental health of soldiers? Perhaps we should lock them down for a cooling off period until they loose their aggressiveness.

It was at this point that Peters did what any good statistician would have done: he refused to look at the statistic in isolation. He asked: is 7.3 a lot, or is it a little? How can you find out? It’s easy: by going to the Bureau of Justice web site and looking at the murder rates per 100,000 in a demographic most similar to that of GIs, which are 18-24 year-olds:

The civilian murder rate is 26.5 per 100,000

which is more than 3.5 times higher than for GIs! Incidentally, the murder rate for 14-17 year-olds is 9.3; and for those 25-34 it is 13.5, both higher rates than for GIs. It isn’t until you reach the the 35-49 year-olds do you find a lower rate at 5.1 per 100,000. As Peters says the Times

unwittingly makes the case that military service reduces the likelihood of a young man or woman committing a murder.

But his best work comes when he notes

In 2005 alone, 8,718 young Americans from the same age group [as GIs] were murdered in this country. That’s well over twice as many as the number of troops killed in all our foreign missions since 2001. Maybe military service not only prevents you from committing crimes, but also keeps you alive?

Peters has called on the Time’s “public editor” Clark Hoyt (who is in charge of correcting errors) to acknowledge the paper’s purposeful character assassination of our veterans. Add your voice to Peters’s: Hoyt’s email is

Update: 16 January 2008.? Good thing I bought a bigger hat.
| 1 Comment

This is what happens when you allow actors to speak without a script

Tom Cruise rules the world

From the video where Mr Cruise identifies scientologists:? “You can see the look in their eyes, you know the ones who are doing it. And you know the spectators, the ones who are going, ‘It’s easy for you’ … I’ve canceled that in my area of my mind. So it’s our responsibility to educate, create the new reality. We have that responsibility to say, ‘Hey, this is how it should be done.'”

Be sure to watch the last minute.


What can we learn about global warming from poor reporting?

From today’s Syndney Morning Herald comes the headline: “Global warming to impact health“.

First, by impact the reporter almost certainly means influence, a more accurate, but far less energetic and “actionable”, word. But never mind that. Our lesson instead comes from the story, one of a breed which appears almost daily in some major newspaper somewhere in the world.

But before we can get to it, you first have to learn, if you do not already know it, the definition of tautology. A tautology is a statement which is always true; that is, no matter what happens in the word, no matter what conditions eventually hold, a tautology will be true. Some examples: “Either it will rain tomorrow or it won’t” and “Marxism is a stupid theory or it is not.”

Here, from the article we are studying, is the lead sentence; it is a tautological fragment, “Rises in temperature produced by global warming could result in an increase in the number of people being admitted to hospital with kidney disease, heart disease and mental illness in Australian cities.” To make this into a grammatically correct tautology we need only add the implied clause “or the rise in temperature will not result in an increase, etc., etc.”

So the reporter has written something which is true, which will always be true, and will be true regardless whether mankind influences the climate or not. But he has written his tautology in such a way to show where his sympathies lie, much as I did in my second example. In any case, we have no grounds for criticizing the reporter on the grounds of accuracy. All such attempts, which I have seen from the skeptical community, are doomed to failure.

We now have to look at the “study” on which the reporter did his article. This will require some work from us, but it is exceedingly important that you understand this study, because it is entirely typical of academic work in this area. You will see more of its kind, and with increasing frequency, so it is imperative that you learn to recognize it and ascertain how to properly criticize it.

Here are the second and third sentences

The study, by a team of academics and senior health professionals from across the country, compared the number of hospital admissions, ambulance trips and the deaths in Adelaide during heat waves, with those in normal weather conditions.

The heat waves – defined as a periods of three days or more in which the average temperature exceeded 35 degrees – produced a seven per cent increase in admissions to hospital and a four per cent increase in ambulance trips.

They also tabulate rates of kidney disease and mental illness under non-heat wave and heat wave conditions, finding these maladies increase during heat waves.

Here is their argument: since, they conclude, more cases of some diseases are present during heat waves, and heat waves will increase with global warming, and that global warming is true, we will see more cases of these diseases.

The structure of their pleading is in perfect logical form, and is correct; that is, their conclusion is true given their premisses. I emphasize: you cannot criticize the form of their argument, since that form concludes something which is true. Or I should say, conditionally true. We will see more disease if it is also true that more cases of some diseases are present during heat waves, etc.

Are their premisses true? I will offer a series of alternate possibilities and likely faults, but I am sure to miss some, which I hope my readers will help supply.

Statistical sample criticisms:

  • Did the authors look through the data to find diseases that increased in frequency during heat waves? If so, it is highly improbable that if we look at future heat wave data, we would see the same high levels of the diseases, most would have “regressed” to their mean level. And other diseases that they did not study will be found to have increased in frequency.
  • What period of data was used?? Presumably, the epidemiology of these diseases have changed through time, certainly “ambulance driving” has.? The time series component to these data should have been accounted for.
  • How many diseases did they find that did not increase in frequency during heat waves? These should have been noted.
  • How many diseases did they find that decreased in frequency during heat waves? These should have been touted as benefits of warming.
  • What were “non heat wave conditions”? Cold waves? All other periods of time? If cold waves, then how many diseases increased in frequency during cold waves? These should have been touted as benefits of warming. If all other periods of time, then they have chosen a poor sample: cold waves should have been separated out.

Medical criticisms:

  • Are there rigorously clear and certain connections between humans living in heat waves and the diseases noted? If not, then the uncertainty associated with each should have been detailed.
  • Again, the diseases increasing in frequency under cold waves were ignored.
  • What benefits for other maladies are there for increased warming? It is foolish to say there are none, for, at the least, fewer people would die from extreme cold.

Technological criticisms:

  • It is not at all certain that, given that heat waves will increase in frequency, people will suffer in them as they suffer now. It is highly probably that technological advances will, for example, increase the availability and efficiency of air conditioning.
  • Medical science, too, will almost certainly increase in efficacy and, with high probability, lessen the number of people susceptible to the diseases under question, therefore, even if heat waves increase, the rate at which people suffer will decrease.

Global warming criticisms:

  • Even if global warming is true, it is not certain, and even unlikely, that heat waves will increase in frequency. Assuming the models which predict warming are accurate, they predict more warming at nighttime and a more evening out of temperatures (reducing the diurnal swing of temperatures) than an increase in severe weather. In any case, the uncertainty inherent in these forecasts of increasing heat waves must be taken into account, and it was not.
  • All other possible benefits of warming were ignored.
  • And, finally, the uncertainty that global warming will continue was not accounted for.

Every criticism I offered did the same thing: increase the uncertainty, or decrease the certainty if you like, that we should have in the conclusions, in my view, to such an extent that the study is nearly worthless, and should not have seen publication.

But the authors were not content with their “findings”, they progressed to naked speculation: said one of them, warming “might also bring a significant increase in previously uncommon diseases such as Dengue and Ross River fever to Australia’s rural communities” and that we “could see both a worsening of existing diseases as well as the spread of diseases usually associated with warmer region.” Of course, we could; it is mere tautology to say we could, but to offer such a prediction without evidence and without an expression of uncertainty can rightly be called fear mongering.

I hope you have learned a little about how to properly criticize studies of this type. But whatever other criticism you offer, you cannot say this study, and others like it, are “not science.” It is science, but it is bad science, poorly executed science, and irresponsible science.