Skip to content

Category: Statistics

The general theory, methods, and philosophy of the Science of Guessing What Is.

August 22, 2008 | 3 Comments

Reportorial entrails and other auguries

“The VP speculation continues” is the headline of the day.

Some reporters camped out at Senator Joe Biden’s house and discovered he had visited a bank and a clothing store. This caused trembling and angst. What do these signs mean? Could Biden be withdrawing cash just in case he’ll soon be far from home? And maybe he bought a new tie at that clothing store! And where do you wear ties? At press conferences, such as those announcing VP picks.

Meanwhile, other pundits wonder if the Great Leg Tingler hasn’t really chosen his VP after all, and that he only said so to stall for time.

Are reporters as thick headed as they appear?

Not one of these bright journalism-school graduates hit upon the most likely reason for the delayed announcement: The longer the deferral the more free air time the Leg Tingler gets. They are being led by the nose and not one of them has the guts to say so.

Note carefully that I am not faulting the Democrat nominee: he is doing just as he should, and nothing less than his opponent will soon do. I am instead dismayed at the simple mindedness of the media—yes, even despite all evidence that I should have expected no less.

August 21, 2008 | 34 Comments

Suicides increase due to reading atrocious global warming research papers

I had the knife at my throat after reading a paper by Preti, Lentini, and Maugeri in the Journal of Affective Disorders (2007 (102), pp 19-25; thanks to Marc Morano for the link to World Climate Report where this work was originally reported). The study had me so depressed that I seriously thought of ending it all.

Before I tell you what the title of their paper is, take a look at these two pictures:

temperature in Italy 1974 to 2003
number of suicides in Italy 1974 to 2003

The first is the yearly mean temperature from 1974 to 2003 in Italy: perhaps a slight decrease to 1980-ish, increasing after that. The second pictures are the suicide rates for men (top) and women (bottom) over the same time period. Ignore the solid line on the suicide plots for a moment and answer this question: what do these two sets of numbers, temperature and suicide, have to do with one another?

If you answered “nothing,” then you are not qualified to be a peer-reviewed researcher in the all-important field of global warming risk research. By failing to see any correlation, you have proven yourself unimaginative and politically naive.

Crack researchers Preti and his pals, on the other hand, were able to look at this same data and proclaim nothing less than Global warming possibly liked to an enhanced risk of suicide.” (Thanks to BufordP at FreeRepublic for the link to the on-line version of the paper.)

How did they do it, you ask? How, when the data look absolutely unrelated, were they able to show a concatenation? Simple: by cheating. I’m going to tell you how they did it later, but how—and why—they got away with it is another matter. It is the fact that they didn’t get caught which fills me with despair and gives rise to my suicidal thoughts.

Why were they allowed to publish? People—and journal editors are in that class—are evidently so hungry for a fright, so eager to learn that their worst fears of global warming are being realized, that they will accept nearly any evidence which corroborates this desire, even if this evidence is transparently ridiculous, as it is here. Every generation has its fads and fallacies, and the evil supposed to be caused by global warming is our fixation.

Below, is how they cheated. The subject is somewhat technical, so don’t bother unless you want particulars. I will go into some detail because it is important to understand just how bad something can be but still pass for “peer-reviewed scientific research.” Let me say first that if one of my students tried handing in a paper like Preti et alia’s, I’d gently ask, “Weren’t you listening to anything I said the entire semester!”

Continue reading “Suicides increase due to reading atrocious global warming research papers”

August 18, 2008 | 13 Comments

Stop making babies to reduce global warming

The other day, as a favor, I posted a scientific article from a friend of mine, Dr H. Harrister, PhD, who conclusively showed that fitter people have larger carbon footprints than do fatter people. You might remember Dr Harrister from his famous paper showing that zombie attacks will increase due to global warming.

Unfortunately, because of sloppiness on my part, several readers came to the conclusion that Dr Harrister, PhD’s paper was satire. That is to say, a joke. Far from it. That paper was just as rigorous and valid as the dozens that now appear monthly in scientific, peer-reviewed journals the world over.

As evidence of that, we have the essay by John Guillebaud, PhD and Pip (yes, Pip) Haye, MD, in the very prestigious British Medical Journal. The title of their work “Population growth and climate change: Universal access to family planning should be the priority.” For my slower readers, I emphasize that they use the familiar euphemism “family planning” for “contraceptives and abortion.”

These eminent authorities start their editorial by claiming

The world’s population now exceeds 6700 million, and humankind’s consumption of fossil fuels, fresh water, crops, fish, and forests exceeds supply.

Their statement is true, it must be true because it’s in a science journal. I suppose I am stupid because I have not seen wide-spread global famine or thirst or lack of lumber or sushi or etc. But these appalling conditions must exist or these men would not have said the use of resources currently “exceeds supply.” I am grateful for having learning something new.

It’s actually worse than this because each year there about 80 million new mouths to feed, or about 1.5 million a week by their calculations, which “amounts to a huge new city each week, somewhere, which destroys wildlife habitats and augments world fossil fuel consumption.” Anybody notice where they’re putting these cities? I haven’t been out to the Dakotas, but the people I’ve met from there have always acted suspiciously. You also can’t trust the Chinese.

Although this paper is, as I have said, scientific, they do make a mistake. They say “In 1798 Malthus predicted that as the population increased exponentially, shortfalls in food supply would be unavoidable.” Actually, Malthus did not say this. Malthus predicted that the population (of any species) will always be as large as the available food supply allows, barring war, disease, and other activities that increased deaths or suppressed births. Mathus’s theory was a steady-state one occasionally effected by “shocks.” But never mind that. Everybody makes this mistake about Malthus.

More importantly, the authors turn to “unmet fertility needs and choices”, by which, again, they mean increasing access to “contraceptives and abortions”; the later word they are unable or unwilling to expose.

They say “economists overlook the fact that, everywhere, potentially fertile intercourse is more frequent than the minimum needed for intentional conceptions.” Economists, those with academic PhDs, might have overlooked intercourse for pleasure, but I can assure you dear reader that I have not. I can’t answer for your own spouses, of course. Anyway, they scientifically state that, even though theory doesn’t predict it, “having a large rather than a small family is less of a planned decision than an automatic outcome of human sexuality.” Now I know!

Because of this mysterious, and anti-theoretical outcome, “Something active needs to be done to separate sex from conception” (emphasis mine). Guillebaud and Haye suggest giving out contraceptives (yes, they finally use that word). I’d say free televisions and cable subscriptions would have the same effect. Either way, handing out condoms and pamphlets explaining their use tends to happen in places where the population get richer and starts caring more about themselves than others, which “is consistent with normal consumer behaviour.”

Prophylactics are not the only recourse we have to discourage the “automatic outcome of human sexuality”. We also have soap operas!

The Population Media Centre [in Iran] uses serial radio dramas or “soaps”. Audiences learn from decisions that their favourite characters make—such as allowing wives to use contraception to achieve smaller and healthier families.

Thank God for government soap operas because, as we all know but rarely publicly state, people really are too stupid to think for themselves, aren’t they? I’d also suggest government-sponsored pictures of dirty diapers on milk cartons so that first thing in the morning as potential parents prepare their frosty flakes, they can see the horrors that await them as the result of the “automatic outcome of human sexuality.”

But what about the global warming menace?

The Optimum Population Trust [where both the authors work] calculates that “each new UK birth will be responsible for 160 times more greenhouse gas emissions . . . than a new birth in Ethiopia.” Should UK doctors break a deafening silence here? “Population” and “family planning” seem taboo words and were notably absent from two BMJ editorials on climate change. Although we endorse everything that those editorials recommended, isn’t contraception the medical profession’s prime contribution for all countries?

Unless I’m reading this wrong—and I admit to being in a different scientific class than our authors—they are advocating that doctors’ “prime contribution” should be contraception and abortion services. So much for healing ills and curing the sick. Well, they are the doctors, not us, and they do correctly note that “Unplanned pregnancy, especially in teenagers, is a problem for the planet.”

They rhetorically ask “Should we now explain to UK couples who plan a family that stopping at two children, or at least having one less child than first intended, is the simplest and biggest contribution anyone can make to leaving a habitable planet for our grandchildren?” The answer is obvious, my dear readers.

Incidentally, Guillebaud is an expert on contraceptives: he “has received fees and expenses from manufacturers of contraceptives for educational presentations, research projects, and short term consultancies.” But so what if he makes an extra buck from the government endorsing his plan? We’re trying to save the plant here, folks.

August 16, 2008 | 5 Comments

Wall Street Journal: Better than a statistics textbook.

On Thursday 14 August, the Wall Street Journal had two excellent articles, which expertly described the statistics and uncertainty of their topics. Several readers have wrote in asking for an analysis of these articles.

1. The first was by Thomas M. Burton: “New Therapy for Sepsis Infections Raises Hope but Many Questions.” Sepsis is a nasty disease that often is the result of other trauma or infection, and is often deadly. Curing it is difficult; usually a third or more of the patients who contract it die. So when a study published by Emanuel Rivers, a doc in the emergency medicine department at Henry Ford Hospital in Detroit, appeared with a new therapy that seemed to have a higher cure rate than traditional therapy, doctors were excited. (Incidentally, I made my first appearance at that same hospital.)

But a few were skeptical. The “questions” in Burton’s title hinge on the statistical methods used in the journal article—which was published in the most prestigious medical journal. Turns out that Rivers did not use all the patients he had entered into his study in the actual statistical analysis. “Statisticians were especially concerned when they noticed that a relatively high proportion of the other 25 — those not included in the final analysis — were either conventional-therapy patients who survived or patients on aggressive therapy who died.”

Why were these patients left out of the analysis? Well, doctor judgment: these 25 patients were not evaluated, at the time, to be as sick, so they were left out. In medical statistics, there is a concept called intent-to-treat, and it means that you must analyze the data putting all patients into the groups that you first put them in no matter what. This procedure is meant to guard against the experimenter effect, which is the boost in results got by the researcher when he, consciously or not, fiddles the patient rolls to get his desired result.

Why wasn’t the original paper criticized on these grounds? A peer-reviewed paper, I should emphasize. Are we saying it is possible that published research could be wrong?

2. Thanks to reader Gabe Thornhill for pointing out another excellent piece by Keith J. Winstein for his article “Boston Scientific Stent Study Flawed.” You might remember Mr Winstein was the only reporter to get the story about boys’ and girl’s mathematical abilities correct.

The story is that Boston Scientific (BS) introduced a new stent, which is an artificial pipe support that is stuck in blood vessels to keep them from being choked off by gunk, called the Taxsus Liberte. BS did the proper study to show the stent worked, but analyzed their data in a peculiar way.

Readers of this blog might remember Chapter 14 of my book: How to Cheat. In classical statistics, an excellent way to cheat, and a method you can almost always get away with, is to change your test statistic so you get the p-value you desire. For any set of data there are dozens of test statistics from which to choose. Each of them will give you a different p-value. For no good reason, the p-value has to be less than 0.05 for a success. So what you do is keep computing different statistics until you find the one which gives you the lowest p-value.

This trick nearly always works, too. It carries a double-bang, because not only can you nearly always find a publishable p-value, nobody can ever remember the actual definition of a p-value. Smaller p-values are usually accompanied with the claim that the results “stronger” or “more significant”. False, of course, but since everybody says so you will be in good company.

Actually, Mr Winstein has two definitions in his piece that aren’t quite right. The first:

Using a standard probability measure known as the “p-value”, it said that there was less than a 5% chance that is finding was wrong


[S]cience traditionally requires 95% certainty that a study proved its premise.

Pay attention. Here is the actual definition of a p-value, adapted to the stent study. For the two sets of data, one for the BS stent, one for another stent, posit a probability distribution which describes your uncertainty in the measures resulting from using these stents. These probability distributions have parameters, which are unknown unobservable numbers that are needed to fully specify the probability distributions.

Now, ignore some of these parameters, and concentrate of just one from each distribution (one for the BS stent, one for the other) and then say that one parameter for the BS stent is exactly equal to the parameter for the other stent. Then calculate a statistic. From above, we know we have the choice of several—and Mr Winstein has an excellent graph showing some possible choices. Here comes the p-value. It is the probability that, if you repeated the same experiment an infinite number of times, that you would see a statistic as larger or larger than the one you actually got given those two parameters you picked were exactly equal.

Make sense? Or is it confusing? I’d say the later. One thing you cannot say is that, for example with a p-value of 0.04, there is a 96% chance that the two stents are the same (BS sought to say their stent was equivalent to its competitor’s). Nor can you say there is a 4% chance you are wrong. All you can say is that there is a 4% chance that if you repeated the experiment many times, each time calculating the same statistic, than one of those statistics would be larger than the one you got (again, given the two parameters are exactly equal).

Whew. A lot of work to get to this point, I agree. But this is it, because nobody—even professorial classical statisticians, which we’ll see in a moment—can actually remember this definition. This is what makes it possible to cheat.

Boston Scientific used something called a Wald test, which is way to approximate the p-value, because often p-values cannot be computed exactly. It is well known, however, that this method gives poor approximations and often gives p-values that are smaller than they should be. However, all this is conditional on the test statistic used being correct, and on the probability distributions chosen for the observable data being correct, and on the parameters you ignored to set up the p-value being ignorable, always huge assumptions. This is why it is strange to see, near the very end of the article, a professor of statistics say that the imperfect Wald method is commonly used but that

Most statisticians would accept this approximation. But since this was right on the border [meaning the p-value was barely under the magic number], greater scrutiny reveals that the true, the real, p-value was slightly more than 5%

The true, the real? The problem here is there is no true or real p-value. Each of the p-values computed by using the different statistics is the true, real one. This is one of the main problems with classical statistics. Another is the persnickety insistence on exactly 0.05 as the cutoff. Got a p-value of 0.050000001? Too bad, chum. Have a 0.0499999999 instead? Success! It’s silly.

Obviously, misinterpreting p-values is a big problem. But ignore that. Winstein and the WSJ have done a wonderful job summarizing a difficult topic. Are you ready for this? They actually got the data and recomputed the statistical tests themselves! This is real science reporting. It must have taken them a lot of effort. If only more journalists would put in half as much work as Mr Winstein, we’d have eighty percent less junk being reported as “news.” In short, read Winstein’s article. He has quotes from Larry Brown, one of the top theoretical statisticians alive, and comments from officials at the FDA about why these kinds of studies are accepted or not.