William M. Briggs, Statistician

The advice which forms the title of this post would be how Don Rickles, if he were a statistician, would explain how not to conduct times series analysis. Judging by the methods I regularly see applied to data of this sort, Don’s rebuke is sorely needed.

The advice is particularly relevant now because there is a new hockey stick controversy brewing. Mann and others have published a new study melding together lots of data and they claim to have again shown that the here and now is hotter than the then and there. Go to climateaudit.org and read all about it. I can’t do a better job than Steve, so I won’t try. What I can do is to show you what not to do. I’m going to shout it, too, because I want to be sure you hear.

Mann includes at this site a large number of temperature proxy data series. Here is one of them called wy026.ppd (I just grabbed one out of the bunch). Here is the picture of this data:

The various black lines are the actual data! The red-line is a 10-year running mean smoother! I will call the black data the real data, and I will call the smoothed data the fictional data. Mann used a “low pass filter” different than the running mean to produce his fictional data, but a smoother is a smoother and what I’m about to say changes not one whit depending on what smoother you use.

Now I’m going to tell you the great truth of time series analysis. Ready? Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses! If the data is measured with error, you might attempt to model it (which means smooth it) in an attempt to estimate the measurement error, but even in these rare cases you have to have an outside (the learned word is “exogenous”) estimate of that error, that is, one not based on your current data.

If, in a moment of insanity, you do smooth time series data and you do use it as input to other analyses, you dramatically increase the probability of fooling yourself! This is because smoothing induces spurious signals—signals that look real to other analytical methods. No matter what you will be too certain of your final results! Mann et al. first dramatically smoothed their series, then analyzed them separately. Regardless of whether their thesis is true—whether there really is a dramatic increase in temperature lately—it is guaranteed that they are now too certain of their conclusion.

There. Sorry for shouting, but I just had to get this off my chest.

Now for some specifics, in no particular order.

• A probability model should be used for only one thing: to quantify the uncertainty of data not yet seen. I go on and on and on about this because this simple fact, for reasons God only knows, is difficult to remember.
• The corollary to this truth is the data in a time series analysis is the data. This tautology is there to make you think. The data is the data! The data is not some model of it. The real, actual data is the real, actual data. There is no secret, hidden “underlying process” that you can tease out with some statistical method, and which will show you the “genuine data”. We already know the data and there it is. We do not smooth it to tell us what it “really is” because we already know what it “really is.”
• Thus, there are only two reasons (excepting measurement error) to ever model time series data:
  1. To associate the time series with external factors. This is the standard paradigm for 99% of all statistical analysis. Take several variables and try to quantify their correlation, etc., but only with a mind to do the next step.
  2. To predict future data. We do not need to predict the data we already have. Let me repeat that for ease of memorization: Notice that we do not need to predict the data we already have. We can only predict what we do not know, which is future data. Thus, we do not need to predict the tree ring proxy data because we already know it.
• The tree ring data is not temperature (say that out loud). This is why it is called a proxy. It is a perfect proxy? Was that last question a rhetorical one? Was that one, too? Because it is a proxy, the uncertainty of its ability to predict temperature must be taken into account in the final results. Did Mann do this? And just what is a rhetorical question?
• There are hundreds of time series analysis methods, most with the purpose of trying to understand the uncertainty of the process so that future data can be predicted, and the uncertainty of those predictions can be quantified (this is a huge area of study in, for example, financial markets, for good reason). This is a legitimate use of smoothing and modeling.
• We certainly should model the relationship of the proxy and temperature, taking into account the changing nature of proxy through time, the differing physical processes that will cause the proxy to change regardless of temperature or how temperature exacerbates or quashes them, and on and on. But we should not stop, as everybody has stopped, with saying something about the parameters of the probability models used to quantify these relationships. Doing so makes use, once again, far too certain of the final results. We do not care how the proxy predicts the mean temperature, we do care how the proxy predicts temperature.
• We do not need a statistical test to say whether a particular time series has increased since some time point. Why? If you do not know, go back and read these points from the beginning. It’s because all we have to do is look at the data: if it has increased, we are allowed to say “It increased.” If it did not increase or it decreased, then we are not allowed to say “It increased.” It really is as simple as that.
• You will now say to me “OK Mr Smarty Pants. What if we had several different time series from different locations? How can we tell if there is a general increase across all of them? We certainly need statistics and p-values and Monte Carol routines to tell us that they increased or that the ‘null hypothesis’ of no increase is true.” First, nobody has called me “Mr Smarty Pants” for a long time, so you’d better watch your language. Second, weren’t you paying attention? If you want to say that 52 out 413 times series increased since some time point, then just go and look at the time series and count! If 52 out of 413 times series increased then you can say “52 out of 413 time series increased.” If more or less than 52 out of 413 times series increased, then you cannot say that “52 out of 413 time series increased.” Well, you can say it, but you would be lying. There is absolutely no need whatsoever to chatter about null hypotheses etc.

If the points—it really is just one point—I am making seem tedious to you, then I will have succeeded. The only fair way to talk about past, known data in statistics is just by looking at it. It is true that looking at massive data sets is difficult and still somewhat of an art. But looking is looking and it’s utterly evenhanded. If you want to say how your data was related with other data, then again, all you have to do is look.

The only reason to create a statistical model is to predict data you have not seen. In the case of the proxy/temperature data, we have the proxies but we do not have temperature, so we can certainly use a probability model to quantify our uncertainty in the unseen temperatures. But we can only create these models when we have simultaneous measures of the proxies and temperature. After these models are created, we then go back to where we do not have temperature and we can predict it (remembering to predict not its mean but the actual values; you also have to take into account how the temperature/proxy relationship might have been different in the past, and how the other conditions extant would have modified this relationship, and on and on).

What you can not, or should not, do is to first model/smooth the proxy data to produce fictional data and then try to model the fictional data and temperature. This trick will always—simply always—make you too certain of yourself and will lead you astray. Notice how the read fictional data looks a hell of a lot more structured than the real data and you’ll get the idea.

Next step is to start playing with the proxy data itself and see what is to see. As soon as I am granted my wish to have each day filled with 48 hours, I’ll be able to do it.

Thanks to Gabe Thornhill of Thornhill Securities for reminding me to write about this.

Around the 4th of July, here in the States, there is a tendency for official weather forecasts to show a probability of precipitation that is lower than it should be. It rains more than the forecasters guess.

The same thing inverted happens around December 25th (the Federally Recognized Holiday That Shall Not Be Named): the forecasts tend to give too high a probability of precipitation. It snows less than the forecasters guess.

This phenomena is well recognized in meteorology where it has long gone by the name of wishcasting: it is also found in many other areas of life, which I’ll talk about below. Wishcasting describes the tendency of the forecaster to tilt his guess toward the outcome which he would like to see, or toward the outcome he knows his viewers would like to see.

Good weather forecasters, obviously, are aware of this tendency and do their best to lessen its influence. But even the best of them tend to get excited when a big storm is on its way, these being matters of great and evident importance, and sometimes issue forecasts which exaggerate the chance of severe weather. Still, the influence of wishcasting is small among professionals, mostly because of the routine evaluation of forecast performance and criticism of peers. People like to pick on weather forecasters, but among any professional group, I have not found any to be better or more reliable than the National Weather Service.

Before we go further, let me answer an objection which might have occurred to you. Why not exaggerate the probability of a storm causing damage since “it’s better to be safe than sorry”? To do this takes the decision out of the hands of person who will experience the storm and puts it into the hands of the forecaster. And that is the wrong thing to do: the forecaster does not know better than his audience what decisions are best. Every person in the path of a storm knows what losses he will face if a storm hits, and how much it will cost him to protect. If people are routinely given exaggerated forecasts, then they will pay the cost of protecting more than they should, and those costs are not insignificant (how much money is being lost by the shops of New Orleans from the protracted evacuation?). You cannot use the forecast as a tool to warn people of dangers which are unimportant to them. It will make them less likely to believe forecasters when real dangers arise. The lesson of Chicken Little is pertinent.

While the Weather Service forecasters do a great job, this is not so among reporters who routinely wildly overstate potential dangers, even when that danger has passed. Anybody who watched television coverage of hurricane Gustav could attest to this. We saw fearless reporter Geraldo Rivera standing in the streets of New Orleans holding a small aneomometer shouting, “There’s a 60 miles per hour, Bob! Wait! A 61!” He bravely leaned into the stiff breeze and held his ground to bring us this breaking news. Of course, anybody who has driven a car and stuck their hand out the window will know that a 60 MPH wind is hardly life threatening.

Well, reporters shading the truth, embroidering facts, neglecting pertinent information, and at times outright lying is by now of no surprise. People have learned to “divide by 10″ any statement issued from a newsroom, so journalists cause less harm than they would if they were taken at face value.

Wishcasting is by no means restricted to weather predictions. I’ll ask you right now, who will be elected president: McCain or Obama? It is difficult to remove the prejudices you have for one candidate or the other and give a good guess. If you love McCain, you are likely to increase the chance of him winning. If you fear Obama’s promised tax increases, that might increase your guess of the chance of him winning if you are naturally pessimistic. To carefully sift through all the evidence and arrive at an unemotional prediction is extremely difficult.

Gamblers often wishcast. “Red hasn’t come up if seven spins, so it’s more likely to now.” Part of this reasoning is due to misunderstanding or not knowing the rules of probability that govern simple games, but part is also due to the desire for the outcome. Wishcasting is prevalent in environmental circles. So much so, that an “activist” who doesn’t embellish is a oddity. Brokers, financial planners, stock pickers, and similar professionals are no less prone to wishcasting.

Wishcasting is somewhat different than the experimenter effect, although there is some overlap. The experimenter effect is when a scientist (or group of them), consciously or not, set up a model to demonstrate the effect they were looking for. A common example is a drug trial. One group is given a new drug, the other an old one or a placebo. If the patients are evaluated by a physician who knows which patient got which drug, it is likely the effects of the new drug will be exaggerated. This phenomena is so well known that the government mandates blinding of medical trials. This is where the physician who evaluates the patients has no idea which treatment the patient has received.

Michael Crichton, physician and author, in testimony to congress, gave an example of this:

It’s 1991, I am flying home from Germany, sitting next to a man who is almost in tears, he is so upset. He’s a physician involved in an FDA study of a new drug. It’s a double-blind study involving four separate teams—one plans the study, another administers the drug to patients, a third assesses the effect on patients, and a fourth analyzes results. The teams do not know each other, and are prohibited from personal contact of any sort, on peril of contaminating the results. This man had been sitting in the Frankfurt airport, innocently chatting with another man, when they discovered to their mutual horror they are on two different teams studying the same drug. They were required to report their encounter to the FDA. And my companion was now waiting to see if the FDA would declare their multi-year, multi-million dollar study invalid because of this chance contact.

His point in this testimony was to show that researchers in global warming are nowhere near as careful as their colleagues in medicine:

[T]he protocols of climate science appear considerably more relaxed. In climate science, it’s permissible for raw data to be “touched,” or modified, by many hands. Gaps in temperature and proxy records are filled in. Suspect values are deleted because a scientist deems them erroneous. A researcher may elect to use parts of existing records, ignoring other parts. But the fact that the data has been modified in so many ways inevitably raises the question of whether the results of a given study are wholly or partially caused by the modifications themselves…

…[A]ny study where a single team plans the research, carries it out, supervises the analysis, and writes their own final report, carries a very high risk of undetected bias. That risk, for example, would automatically preclude the validity of the results of a similarly structured study that tested the efficacy of a drug.

Wishcasting meets the experimenter effect when the results from a non-blinded experiment are exaggerated to “raise awareness” of the potential horrors that await us if we do not heed the experimenters. Sometimes this exaggeration is done on purpose, as with the weather forecaster who feels his viewers would be “better safe than sorry”, and sometimes the overstatement is unconscious because the forecaster has not recognized his limitations. Scientists often feel they are special and able to avoid the frailties that plague the rest of us, but of course, they cannot; they are still human.

It is nearly impossible to disentangle experimenter effect from wishcasting in any situation, nor can we easily identify the constituent facts and their relevance used by a forecaster in producing his forecast. To do so essentially means producing a rival forecast and is a laborious process.

What we can do (this is my line of country) is to check how good the actual performance of a forecast is. If the forecast routinely fails, we can say something has gone wrong. Just what requires more work: was it bad data, mistaken theory, wishcasting, or something else? If the forecast routinely fails, we are rational to suspect it will fail in the future, and that the theories said to underly the forecast might be false. If the forecast fails, we are also right to question the motives of the forecaster, because it is these motives that influence the presence or amount of wishcasting.

These cautions do not just apply to weather or climate forecasts, but in all areas where routine predictions are made. Could you be making more money in your stock portfolio or office football pool, for example? Generally, wishcasting takes places when forecasting complex systems, like the weather, climate, or any area involving human behavior. It’s much less likely in simple situations, like how much this electron will move under a certain applied force, or what will happen when these two chemicals are mixed. But we’ll save complexity for another day.

Gustav is weakening and steering to the west of the Big Easy.

The Air Force regularly sends in its “hurricane hunter” aircraft, and the last observation of about a hour ago showed central winds “generously described” at 100 knots, and those probably falling. The National Hurricane Center reports that the AF plane didn’t even find an eye wall, though radar reports an open (therefore weaker one) over the south.

The forecasters also report “WATER VAPOR IMAGERY ALSO SUGGESTS A DRY INTRUSION AND A RESTRICTION OF THE UPPER-LEVEL OUTFLOW OVER THE SOUTHERN PORTION OF THE HURRICANE.” What that means in English is that the storm will very probably get weaker. (We—I used to be a National Weather Service forecaster—always had to type forecasts in all upper case. This is a throwback to teletypewriter days.)

“ALSO THE CLOUD PATTERN HAS BECOME A BIT MORE RAGGED ON GEOSTATIONARY SATELLITE PICTURES. BASED ON CURRENT TRENDS AND THE PROXIMITY TO THE COAST…NO SIGNIFICANT INCREASE IN STRENGTH APPEARS LIKELY PRIOR TO LANDFALL.” That one doesn’t need any translation.

Make no mistake, however. Gustav is still a big storm and those in his path should get out of his way. It will absolutely cause damage and cost a lot of people a lot of money.

But the wide-spread death and destruction so avidly hoped for by the radical left (see two posts ago) Michael Moore, Former Democratic National Committee Chairman Don Fowler, inter alia now seems unlikely.

What a sad day!

The weather also spoils the media’s fun. You could almost feel the anticipation and eagerness in the news rooms yesterday. Reporters irresponsibly echoed New Orleans’ mayor’s foolish statement calling Gustav the “storm of the century.” This kind of idiotic hyperbole, while typical, is easy to spot, so why does the media regularly give it so much play?

I just turned on the news and they showed a reporter, ridiculously overdressed in rain gear that would hold back a flood, giving his best effort. He shouted in the microphone, held his hat as the breeze and drizzle assaulted him. What a trooper.

“Zombie attacks to increase due to global warming” rang the headline on this blog seven moons ago. So said eminent zombiologist and scientist Dr Harrister, BS, MS, PhD, OMGWAG.

Who would have guessed that Bob Hope was ahead of us all?

I’ll tell you who! Pepsodent! With irium!

Thanks to Rob Avrech for reminding us of Hope’s wisdom.

Quick Quiz: what do you call an exceptionally nervous busybody who perpetually overestimates risk and on whose lips are forever the phrase, “Something must be done!”

Answer: That person is an activist—activism is a manner of life which nowadays can even be proclaimed a profession.

What, then, do we call somebody who rationally attempts to quantify risk and who soberly (on most days of the week) weighs his options and sometimes proposes that the best course of action is no action at all?

That person is an inactivist.

Isn’t that a great name? I love it! But I didn’t think of it. Frank Bi, who runs a site called The Journal of Inactivism, did.

Old Frank’s intent is that the label be taken ironically. To him, an inactivist is to be despised. Let me tell you something about irony, Frank. There’s an art to it that few possess; its use requires a rare talent. I sometimes flatter myself that I can successfully wield this heavy sword, but I fail nearly every time, as regular readers of this blog can attest.

And so have you failed, Frank, but do not despair. We can rescue your neologism and put it to good use. It can lead a second life of good service.

Thus, I counsel that we adopt the moniker “climate inactivist” at once. Just look what it has going for it.

“Climate skeptic”, a term many favor, is apt to be misleading. I, for example, am not skeptical that there is a climate. Inactivist, however, neatly captures and succinctly describes the attitude of many of us.

We, who do not deny that mankind influences climate, even sometimes harmfully, but who reckon that our uncertainty in the mechanisms of the hideously complex global climate system and the imprecision its forecasts, coupled with the glut of extravagant and ridiculous claims of evils that await us, are not strong enough evidence to yet warrant government-imposed mandatory taxes and regulation. We, who do not deny that that day might come, and who do not discourage voluntary and personal actions. We propose to take no action until our certainty is much stronger.

We propose to be inactive—we are inactivists.

This proposal will be voted upon at the next secret meeting.

Incidentally, to take us on a tangent to an unexpected dimension, Scott Kurtz presents the evil Statistician Magician! (I don’t have a cape.)

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:

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!”

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The famous writer Roger Kimball has issued a challenge:

Name the silliest argument to be offered by a serious academic in the last 25 years and to be taken up and be gravely masticated by the larger world of intellectual debate.

A leading contender is Global Warming. Kimball’s own entry is Francis Fukuyama’s “end of history” thesis.

Kimball’s rules of the contest:

I’ll collect proposals for the next week or two and then announce the winner. (The decision, from which there is no appeal, will be determined by a committee staffed, overseen, and operated entirely by me.)

This tournament reminds me of the one issued by philosopher David Stove, who sought to find The World’s Worst Argument. The winning entry was entered by Stove himself:

We can know things only: as they are related to us; under our forms of perception and understanding; insofar as they fall under our conceptual schemes, etc. So, we cannot know things as they are in themselves.

By “worst”, according to Jim Franklin, his literary executor (Stove is dead) and student, Stove “meant that it had to be extremely bad logically and also it had to be very widely believed.” (Quote is near the end of the link.)

We’ll discuss Stove’s worst argument another time, but the thing to notice now is this. Since he was so familiar with bad arguments of every stripe, Stove capered to the finish line. All other entries didn’t have a chance. It is probably the same with Kimball: he knows too many appalling arguments so it will be difficult to beat him.

Readers of this blog might also nominate “Global warming” for Kimball’s contest, but the category is ambiguous, there is no specificity to it. Of course there is global warming, and mankind is certainly responsible for some of it (think of thermometers placed in urban settings that have grown in population through time). The entry has to be clarified before it has a chance. I don’t think Al Gore will collect this prize.

It will be difficult, therefore, to beat the “End of History” nonsense. This is Fukuyama’s thesis, first promulgated at the end of the Cold War, that “The end of history as such” has been reached; that we have realized “the evolution and the universalization of Western liberal democracy as the final form of human government”; and that “the ideal will govern the material world in the long run.”

(As Russia is clipping Georgia, I realize I should have written “First Cold War” in the previous paragraph.)

As Stove himself (Kimball has edited a volume of his writings) said

[T]he mixture which Fukuyama expects to freeze history forever–a combination of Enlightenment values with the free market–is actually one of the most explosive mixtures known to man. Fukuyama thinks that nothing will ever happen again because a mixture like that of petrol, air, and lighted matches is widespread, and spreading wider. Well, Woodrow Wilson thought the same; but it is an odd world view, to say the least.

It’s a strong contender, this silly argument, and will likely win. But we shouldn’t acquiesce without a fight. Here is my entry (well, it’s a modification of my entry; I clicked “submit” too quickly):

Moral Equivalence

This is the thesis that all ideas are ethically commutable. Moral equivalence often goes by the terms “Diversity” and “Multiculturalism.”

Diversity, as in “we value diversity in our student body.” One major ivy-league university, for example, states that it “is committed to extending its legacy recruiting a heterogeneous faculty, student body and staff; fostering a climate that doesn’t just tolerate differences but treasures them [etc.]” You cannot now find a university that isn’t constantly and loudly devoted to diversity.

However, we can be sure that by this they do not—and should not—mean intellectual diversity. This should be obvious. For if we merely wanted to increase intellectual diversity, we would create classes and recruit subject matter experts in “How to Murder”, “Advanced Pedophilia”, “Creative Robbery”, “Marxist Theory”, or similar idiocies. You often hear conservatives ask to increase intellectual diversity on campuses; conservatives are arguing poorly, because they really mean they want to increase conservative thought.

Diversity, then, cannot mean intellectual diversity. Therefore, to “increase diversity” usually means to “without regard to merit, forcibly manipulate the ratios of student/faculty races so that it matches that of an (unstated) specific goal.” Of course, this implies quotas, which is to say, legalized discrimination based on race. Incidentally, statistically speaking, it is nearly impossible to achieve “diversity” without resort to forced quotas—I’ll talk about this another time.

Multiculturalism is just as bizarre. I have often thought it would be instructive to set up a “Multiculturalism Booth” at a college fair. Participants would take part in common rituals of many different cultures. For example, there would be the stoning of homosexuals, the honorable murder of raped women, the clitorectomy ring toss, a foot race whereby the losers are killed and eaten, and so on. Naturally, the booth’s staff will be equipped with native costumes and pamphlets describing the history and cultural relevance of each topic. This is meant to be educational, after all. At the end of the day, those that survived would be given a survey asking their opinion on the importance of multiculturalism.

How many participants would finally admit that all cultures are not equal, that some are better than others?

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.

The following is a scientific study:

1. INTRODUCTION

Ever since the Supreme Court has (wisely) ruled that carbon dioxide is a pollutant, the number of people who have been made exceptionally nervous has increased nearly exponentially (exponentially is a mathematical term). It is up to science to discover ways of reducing this vile gas, to root out its sources, and suggest interesting ways of scientifically punishing environmental malefactors.

Thus, it follows that it is the duty of every single person to reduce their carbon footprint in every conceivable way, and to do so in the shortest amount of time humanely possible before disaster strikes.

The purpose of this scientific paper, therefore, is to bring to mind a particular activity that had previously been assumed virtuous but under the unerring eye of science has proved to be pernicious. That activity is exercise.

2. MATHEMATICAL CALCULATIONS

We accept, as we must accept after the highest court in the land said so, that carbon dioxide (CO₂) is a pollutant. Many common people—unfortunately, those without sufficient educations—are not aware that each time they exhale they are adding to the excessive burden of CO₂ in our precious atmosphere. This is because humans inhale oxygen into their lungs with each breath—oxygen that is forever depleted from the air, because that oxygen is processed into CO₂.

Each exhaled breath contains a certain amount of CO₂. The exact amount is a function of total lung capacity, residual lung volume, and vital capacity, as well as related to other measures of pulmonary function. In this paper, we make the reasonable approximation that variations in the amount of CO₂ exhaled per breath are negligible, that is, the amount of exhaled CO₂ is fixed for all people, except in two ways which we note below. It is important to recognize, however, that most of this human CO₂ outgassing takes place in the troposphere (troposphere is a meteorological term), in the boundary layer. Shockingly, this is also where most people live.

The main source of variation of exhaled pollution (CO₂), the intra-person variability, is due to respiration rate (RR). Higher RRs mean more breaths per hour and therefore more pollution added to the atmosphere, and therefore the more likely we are to experience runaway greenhouse effects. Therefore, people with lower RRs have smaller carbon footprints (or lungprints as we should properly say) than those people with higher RRs.

Let LS = lung size. The average resting breath for an adult North American human male contains about LS_rest = 325 ml of air. This expands to twice this under aerobic stress (exercise), to about LS_stress = 650 ml of air. There will be variations in these values, of course, but we scientifically dispense with this uncertainty.

Because all tissue in the human body has to be oxygenated lest it turn sour, it implies that the fatter (larger) a person is the more oxygen they consume. This is mitigated somewhat because those at that highest scale of fatness, the couch potatoes, tend to engage in very little movement. And since movement means using muscles, and muscles rely on oxygen as part of their fuel, less movement means less oxygen usage (or utilization, if you prefer).

Since the humans under our consideration are assigned to have equal lung volume and other pulmonary functions, more oxygen usage is equivalent to higher RRs. As explained above, more oxygen usage is directly proportional to more CO₂ creation. To be explicit: higher RRs give rise to higher amounts of CO₂ degassing to the atmosphere. Not all of the air inhaled in processed into CO₂. Let the fraction of each breath that is converted to pollutants be f_co2.

The RR of couch potatoes is likely to be even and vary little throughout any 24-hour period because of their habit of remaining as stationary as possible. This rate will be slightly higher than thin, non exercising people, and higher than exercising people at rest because couch potatoes’ larger bodies need more oxygen. Studies have shown that this rate is RR_CP = 14 breaths per minute. Thus, we present equation 1, the amount of pollutant added (PA) to the atmosphere per day for this class of individual:

Eq. 1    PACP = RRCP x LSrest x fco2 x 60 minutes hours-1 x 24 hours day-1

This equation is unlikely to be wrong because it has been written in the proper mathematical format. It also uses scientific notation, and your author has a PhD.

Equation 1 can be contrasted to a similar equation for those who engage in excessive exercise, which is defined as 22 hours of calm followed by two hours (not necessarily contiguous) of frenetic bursts of ludicrous activity. The RR for exercisers at rest is known to be RR_Ex:rest = 12 breaths per minute. While engaged in stressful activities this rises to RR_Ex:stress = 30 breaths per minute. Thus, the daily amount of PA for exercisers is

Eq. 2    PAEx = RREx:rest x LSrest x fco2 x 60 minutes hours-1 x 22 hours day-1 + RREx:stress x LSstress x fco2 x 120 minutes

Substituting the precise values into equations 1 and 2 gives

Eq. 1b    PACP = 14 x 325 x fco2 x 60 x 24 hours

which equals

Eq. 1c    PACP = 6552000 x fco2

And

Eq. 2b    PAEx = 12 x 325 x fco2 x 60 minutes hours-1 x 22 hours day-1 + 30 x 650 x fco2 x 120 minutes

which equals

Eq. 2c    PAEx = 5148000 x fco2 + 2340000 x fco2 = 7488000 x fco2

which makes the ratio of PA_Ex to PA_CP equal to

Eq. 3    PAEx/PACP = 1.142857

Conveniently, we do not need to know the exact value of f_co2, as it cancels in the equation.

3. CONCLUSIONS

Through the strictest scientific procedures, the same as those used in a myriad of studies of this type, we have conclusively proven that those people who exercise have a carbon foot (or lung) print 14.29% higher than those who, altruistically it turns out, lie around on the couch. Future studies will examine the additional benefits of progressing to a drunken stupor, a state in which minimal oxygen usage is obtained.

It might be argued that CPs eat more, thus they increase the amount of CO₂ added because of their increased food intake. But this argument is specious when contrasted with the habits of the very fit, defined as people who typically motor to Whole Foods in their SUVs to buy only “organic” comestibles, the creation of which produces far more CO₂ than does, say, manufacturing bags of Cheesy Puffs, the chosen snack food of most couch potatoes. Plus, of course, those who exercise more actual consume a greater amount of food than do lazy slobs (this is scientifically true).

The policy implications of this study are obvious: people must be discouraged immediately from exercising. They should be taught the immorality of it, how their narcissistic habits unnecessarily add to carbon burden of the atmosphere, thus endangering the fragile climate system, and therefore the future for our children.

If we can each stop just one jogger from donning his multi-colored, garish shorts and trotting through the neighborhood, we will have done the Earth a tremendous service.

Editor’s note: please help disseminate this scientific study as widely as possible. Inform journalists of the perilous and frightening nature of its conclusions. The time to act is now.

A number of mixed items today, mostly with the theme that Experts are often too sure of themselves.

• The organization GRASP, among many others, until yesterday warned of the “imminent extinction faced by gorillas” and other primates (not humans). NASA, an organization of experts, has a page called “Gorillas in the Midst of Extinction.” They used sophisticated, powerful, high technology satellites to count gorillas “giving scientists and conservationists” a way to count gorillas. The phrase “scientists and conservationists” must mean there is a difference between the two types of creatures. Anyway, the previously (?) communist magazine New Scientist recently had an article called “Ebola pushes gorillas towards extinction” (in the late 1990s there were several books published warning of the same fate for homo sapiens sapiens).

  And then yesterday came a report by a group that unexpectedly came upon a troop of about 125,000 gorillas in the Congo, which more than doubled the previous estimate of the number of gorillas alive. Jillian Miller, the director of the conservation group Gorilla Organization, shockingly admitted (quoted in today’s New York Post), “I think the lesson for conservationists today is that, yes, the world is full of surprises. There’s a lot of uncharted territory.” I wonder if she’ll still feel the same way during the next round of fund raising.

• “Bubble fusion” researcher Rusi Taleyarkhan’s research was burst at Purdue this past week. This is the guy who claimed in 2002 he could induce fusion using the force of collapsing tiny bubbles (the learned word for bursting bubbles is cavitation). The claim was always silly, which is fine, because there are more than enough silly ideas that pass for “research” in academia. The press and others originally bought the idea, however, and surely there will be some people who will always believe, just like there are still some who tout cold fusion. But the claim was too silly for some, who were angered by Taleyarkhan, and they sought to punish him.

  This week’s Science magazine has an article (subscription required) on how Purdue is castigating Taleyarkhan. They suspected he fudged his data, but couldn’t prove it, so like the feds with Al Capone, they got him on a technicality, a move that I hope they are not proud of. Turns out that Taleyarkhan wanted a second author on a paper so that the paper would appear stronger: supposedly, more authors means less likelihood of cheating. So he showed the paper to a graduate student who made changes and recommendations, and then Taleyarkhan put the grad student’s name on the paper. Bingo! Research misconduct! cried the judges. Well, maybe, but if so, then roughly 98.3% of all academics are guilty of the same crime. People often, for a host of reasons, politics, fear, friendship, tit for tat, habit, and on and on, put names of people on papers even though those people had little or nothing to do with the work. Ah well. Poor Taleyarkhan.

• For fun, we have a list of the Top 30 Failed Technology Predictions from the List Universe. Here’s #2, from Mr Bill Gates, a well known rich person who lives near Seattle: “We will never make a 32 bit operating system.” And #8 from Lord Kelvin, who was a mathematician and physicist, and president of the British Royal Society, 1895: “Heavier-than-air flying machines are impossible.”

  Ho ho ho, we say to ourselves when we read these prognostications. How stupid can they be! We experience mirth. But that is exactly the wrong emotion. You might despise Bill Gates, but he is an incredibly bright person, an expert among experts in his field. Kelvin, who you probably haven’t heard of, was one of the smartest people who ever lived (not at the top of the list, to be sure, but ahead of all of us). These, and the other people with quotes on the List Universe page, were masters, yet they made remarkably huge mistakes.

  You must also remember that when these men, superior in perception to their peers, made these predictions, there were not hosts of others saying the opposite. Most people believed the predictions, and with good reason. These experts had often been right before. What we should take away from this list is an increased skepticism, a belief that experts are not nearly right as often as they’d like us to think they are. Doubt, therefore, is the proper emotion.