William M. Briggs

There’s Nancy Pelosi, Six Gun of State strapped to her side, the sun burning overhead. And there’s you, armed only with Freedom, thirty paces off. It’s a standoff! If you blink, it’s all over.

You have one advantage: all those non-deductible surgeries she’s had have made it impossible for her to squint against the noonday sun. You can still get the draw on her!

But the clouds are gathering. You’ll have to act fast. You have one more chance to be allowed to take care of yourself. Call or email your representative today, tomorrow at the latest. Put the fear of non-re-electability into ‘em.

Obamacare, by rooting through your wallet, would provide many with insurance (and those gifted with your largess would be asked nothing in return for it). But having insurance is not equivalent to having health. Although some might mistakenly believe that they want insurance, what they really want is health. And the best way to deliver that is not via a government-controlled health-care bureaucracy. Here’s why.

Mr Obama has placed himself in front of many teleprompters and has been prompted to cast the most outrageous aspersions against insurance companies. The party line is that they are evil, incorrigible. This is so, the reasoning goes, because insurance firms refuse to provide some people health care, and because they receive a profit for their services.

Yet socialized medicine, we are assured, will, by dousing it with regulatory holy water, exorcise the evil from insurance companies. Begone vile profits!

But somehow, in that wisdom they are always touting, Democrats have forgotten that insurance companies do not provide health care. They provide money when they are on the losing end of a bet. You wager you’ll get sick, they say you won’t. If you do get sick, they pay out. If you don’t, they keep your money. A fair transaction entered into freely by both parties.

What would a rational bookie do if you were to say to him, “I’m already sick. I want to bet you that if I get sick, you’ll pay me a lot of money.”? He’d ask that you re-take your elementary math class, that’s what. Yet Mr Obama would require your bookie, the insurance company, to take your bet. He wants to guarantee that the insurance company pays out on what is a losing bet.

What necessarily follows from this? The insurance company must raise its rates. It must charge more to its other clients to make up for its guaranteed, government-mandated loss. The cost of insurance—to be perfectly clear—will rise.

Not only that, but insurance companies will be required to set up internal offices, well stocked with lawyers and accountants, to communicate to the government that they are following the complex regulations set over them. That costs money, so insurance companies will have to raise their prices yet again.

Government, too, will have to set up its own offices that talk back to the insurance companies and to the people it’s giving free money to. The feeding and care of these government employees cost lots of cash.

The money the government requires will come from you. You will not be allowed to refuse to pay it. The extra money the insurance companies must charge will also come from you. Ordinarily, you would be allowed to refuse to pay that. But the law will be such that you can’t turn the private insurance companies down, either.

What’s the worst part? Absolutely none of these vast amount of monies will go towards improving health care. Since all that money is necessarily funneled to a bureaucracy, less will be available to spend on health care improvements.

We have returned to the inevitable Progressive Law of Unintended Consequences. Government meddling, motivated by the best of intentions, when it tries to improve health care and make it cheaper, will cause it to become limited and more expensive.

Further—and perhaps its true purpose after all—it will create a dependency on itself. It will, by law, mandate reliance on itself. The health bureaucracy will become self-perpetuating. And since every bureaucracy ever known has become larger and less efficient through time…well, you can connect those dots.

My solution? Tax the health care money provided from companies to their employees as income. Make that money income. Let people know exactly how much of their own money they are spending on insurance and care. The major reason health care has become so expensive is that nobody knows what anything costs. This opacity has given birth to a private bureaucracy, and as we’ve seen any such creature drives costs up.

I don’t have space to convince you that this solution is ideal. But I hope I’ve shown you that Congressperson Pelosi’s solution is suboptimal. And we haven’t even discussed how, since the government will be paying the bills, they’ll use that leverage to forbid you certain activities (smoking, eating salt, drinking soda pop, etc.), or to require you to perform certain others.

Just remember what P.J. O’Rourke said: “If you think health care is expensive now, just wait until it’s free.”

Update An anonymous reader tells us of a Rally in Washington D.C. on the 16th. Details here.

Update 2 Here, from The Hill is a list of the Democrats leaning on voting No. All Republicans are expected to vote no.

Read Part I

The Analysis (cont.)

Two problems arise when comparing a model’s integration (the forecast) with an analysis of new observations, which are not found when comparing the forecast to the observations themselves. Verifying the model with an analysis, we compare two equally sized “grids”; verifying the model with observations, we compare a tiny number of model grid points with reality.

Now, some kinds of screwiness in the model are also endemic in the analysis: the model and analysis are, after all, built from the same materials. Some screwiness, therefore, will remain hidden, undetectable in the model-analysis verification.

However, the model-analysis verification can reveal certain systematic errors, the knowledge of which can be used to improve the model. But the result is that the model, in its improvement cycle, is pushed towards the analysis. And always remember: the analysis is not reality, but a model of it.

Therefore, if models over time are tuned to analyses, they will reach an accuracy limit which is a function of how accurate the analyses are. In other words, a model might come to predict future analyses wonderfully, but it could still predict real-life observations badly.

Which brings us to the second major problem of model-against-analysis verification. We do not know actually how well the model is performing because it is not being checked against reality. Modelers who rely solely on the analysis model-checking method will be—they are guaranteed to be—overconfident.

The direct output of most climate and weather models is difficult to check against actual observations because models makes predictions at orders and orders of magnitude more locations than there are observations. Yet modelers are anxious to check their models at all places, even where there are no observations. They believe that analysis-verification is the only way they can do this.

This is important, so allow me a redundancy: models make predictions at wide swaths of the Earth’s surface where no observations are taken. At a point near Gilligan’s Island, the model says “17^oC”, yet we can never know whether the model was right or wrong. We’ll never be able to check the model’s accuracy at that point.

We can guess accuracy at that point by using an analysis to make a guess of what the actual temperature is. But since model points—in the atmosphere, in the ocean, on the surface—outnumber actual observation locations by so much, our guess of accuracy is bound to be poor.

MOS

Actual observations can be brought into the picture by matching model forecasts to future observations and then building a statistical model between the two. This is called model output statistics, or MOS. The whole model, at all its grid points, is fed into a statistical model: luckily, many of the points in the model will be found to be non-predictive and thus are dropped. Think of it like a regression. The models’ output are like the Xs, and the observations are like the Ys, and we statistically model Y as a function of the Xs.

So, when a new model integration comes along, it is fed into a MOS model, and that model is used to make forecasts. Forecasters will also make reference to the physical model integrations, but the MOS will often be the starting point.

Better, MOS predictions are checked against actual observations, and it is by these checks which we know meteorological models are improving. And those checks are also fed back into the model building process, creating another avenue for model improvement. MOS techniques are common for meteorological models, but not yet for climatological models.

Measurement Error

MOS is a good approach to correct gross model biases and inaccuracies. It is also used to give a better indication of how accurate the model—the model+MOS, actually—really is, because it tells us how the model works at actual observation locations.

But MOS verification will still given an overestimate of the accuracy of the model. This is because of measurement error in the observations.

In many cases, nowadays, measurement error of observations is small and unbiased. By “unbiased” I mean, sometimes the errors are too high, sometimes too low, and the high and low errors balance themselves out given enough time. However, measurement error is still significant enough that an analysis must be used to read data into a model; the raw data measured with error will lead to unphysical model solutions (we don’t have space to discuss why).

Measurement error is not harmless. This is especially true for the historical data that feeds climate models, especially proxy-derived data. Proxy-derived data is itself the result of a model from some proxy (like a tree ring) and a desired observation (like temperature). The modeled—not actual—temperature is fed to an analysis, which in turn models the modeled observations, which in turn is physically modeled. Get it?

Measurement error is a problem is two ways. Historical measurement error can lead to built-in model biases: after all, if you’re using mistaken data to build—or if you like “inform”—a model, that model, while there is a chance it will be flawless, is not likely to be.

Plus, even if we use a MOS-type system for climate models, if we check the MOS against observations measured with error, and we do not account for that measurement error in the final statistics (and nobody does), then we will be too certain of the model’s accuracy in the end.

In short, the opportunity for over-certainty is everywhere.

Read Part I

Today’s post is at Pajamas Media: President Obama To Make Poverty Permanent.

The Obama administration will create a new definition of poverty, one that changes as a function of how much money everybody has. The more everybody has, the higher the poverty level.

This, of course, guarantees that “poverty” will always be with us.

Would you check the results of a model with another model? Before you answer, be sure you know what the question is.

A model—whether it is physical, statistical, mathematical, or some combination—is an algorithmic device designed to make predictions about some observable thing. You want today to know that price of tomorrow’s Dow Jones Industrial Index? There are models for that; usually statistical models.

You want today to know whether it will rain in Detroit tomorrow so that you can decide whether to plant your crops in the old lots that used to contain houses? There’s a model for that; a physical-statistical weather model called MOS (model output statistics; see Part II).

Now, how would you, assuming you are not an expert in these matters, check the accuracy of your model? Would you (a) compare the model’s predictions with what actually happened, or (b) produce another model and check the results of the first model against the predictions of the second?

The right answer is (a), of course, but the problem is that there are two ways to interpret “what actually happened.” You probably thought it meant “what happened in the future.” Now, it is the great shame in the field of statistics—both in the dismal way it is taught and the worse way it is practiced by most—that (a) is nearly always is interpreted to mean “what happened in the past.”

Nearly all—the exceptions to this are rarer than sober Paul Krugman columns—statistical models, and many physical models, are checked against the data that was used to fit, or create them. Since it is an elementary theorem that any model may be made to fit perfectly—not just closely, perfectly—to any set of historical data, to claim that your model is good because it fits old data well is a hollow boast.

This is the reason for the great overconfidence of experts who build and use models. And don’t think it doesn’t matter, because it does. People in charge of us makes decisions and set policy based on these models frequently. We are at the mercy of bad statistics.

Weather and Climate Models

But it’s not all bad. It is to the great glory of meteorological models that they are usually—in practice, I mean—checked against what happened in the future. Weather models have the advantage of a constant stream of model predictions and future observations. Discrepancies between the two are noted quickly and used in tweaking the models so that they perform better in the future.

Anybody who cares to look will discover that the performance of meteorological models has improved dramatically over the last thirty years. Of course, people’s expectations of accuracy has also increased, so that the level of grousing about weatherman has remained constant. Human nature.

Climate models are in a different category. So far, all they can boast about is how well they fit the data used to build them, which we have just seen is no great shakes. This being true, those who use climate model output should be humble, they should be cautious, even timid about their prognostications. And that’s just what we see in practice, right?

Actually, it’s still worse, because climate modelers—and in their development stages, weather modelers—answer (b) to that question above. They check their models against the output of other models. How could this be?

The Analysis

Climate/weather models take current observations as input and produce forecasts of future observables as output. But these physical models cannot take observations raw, like statistical models can. They must first process those observations so that they fit into the model environment. This assimilation is called an analysis. Analysis is a model itself.

Climate/weather models are run on grid-like structures, but observations come irregularly: we do not have equally spaced observations over the surface of the Earth and through the atmosphere. To operate, the observations have to be placed on the model grid. The analysis, then, is a sort of interpolation that does this. This is not a detriment; it is a necessary step to get these models to run.

Once the analysis is complete, the model is integrated forward in time to produce a forecast. OK so far? Because it’s about to get tricky. At that future point—the time of the forecast—come new observations. Ideally, the climate/weather model’s output would be checked against these actual observations, at only the irregularly spaced sites where they are taken. These observations are, are the truth, the whole truth, and the only truth.

But that’s not what happens. Instead, these new observations are read into the model in a new analysis cycle. This interpolates these new observations to the model grid. Then the old model integration is checked against this new analysis.

Thus, the model’s accuracy is checked with another model.

In Part II: MOS and measurement error

The deadliest force known to man—The Law of Unintended Consequences—has struck again. This time it has led to the deliberate slaughter of sea lions by armed agents of the U.S. Government.

What happened was this: A dam was built in Bonneville, east of Portland, Oregon. As dams will, it created a barrier for both water and anything else that would otherwise traverse the river. This “anything else” included fish, like salmon. And not just any salmon, chinook salmon.

See, chinook salmon are one of the lucky few species protected by a law meant to preserve their breed: The Endangered Species Act. Somebody notices an animal, preferably photogenic, is low in number. If a bureaucracy in Washington D.C. agrees, through a mysterious process, that more of that animal is better than few, then the animal is placed on a list.

Once ensconced, all Heaven and Earth will be moved in an effort to keep that species a going concern. In this case, I mean that literally: the Earth was moved for the chinook. The Bonneville dam was shifted and modified to includ a “ladder”, a sort of stepped, inverse water slide, which the chinook (or other fish) could use to cross the dam.

But just like the water slide at your amusement park, the Bonneville Ladder created long lines of salmon waiting to take their turn.

Which wouldn’t be that bad—salmon do not have busy schedules—except the lingering fish attracted the attention of some sea lions, who, as do humans, find salmon a tasty treat. The feast began.

Now, salmon are stubborn, inflexible creatures. Any other species would see that gathering together in a shallow pool where the only line of escape is a single-file passage is not a brilliant move. They would move on. But the salmon stay; they come back year after year, and a lot of them find themselves in the bellies of sea lions.

Sea lions, however, are smart: they remember where and when the salmon will show. So they show, too. This is to the sea lions’ benefit, obviously. If they don’t have to worry about dinner, then they can concentrate on more important matters, like breeding. Their numbers are increasing.

But the sea lions, our government has decided, are breaking the law! Killing and eating chinook salmon, which are protected by the Endangered Species Act, is equivalent to murder, which is everywhere a crime (except under communism).

Many sea lions have been arrested! According to a San Jose Mercury News report, once the perpetrators are nabbed, they are held for 48 hours.

After they are processed, some of these sea lions are carted off to jail—in the form of aquariums, zoos or “similar” facilities. These sea lions are never heard from again. Some are let go with a warning.

But the killing spree has gotten so bloody—officers estimate 4,489 salmon were killed and eaten last year—that a most wanted list of the worst of the offenders has been created. These hardened, “repeat offenders” are “identified by scars” or by tattoos that they had carved into them when they were last in prison (“numbers that were branded on them by researchers”—a.k.a. marine criminologists).

SWAT teams have been dispatched to where renegade gangs of sea lions gather. These cops use standard riot control methods to disperse the population, such as “dropping bombs that explode under water, and firing rubber bullets and beanbags”.

This is sometimes not enough. Sometimes these confrontations devolve into pitched gun battles between the police and the recalcitrant sea lions. The fights never last long, mostly because the sea lions have not yet learned how to fire back.

These melees have taken their toll. Last year, 11 sea lions were shot and killed when they refused to stop eating salmon. So far this year, there has only been one deadly confrontation, when the sea lion “Whiskers Malone” was shot multiple times after showing his hind flipper to police.

Since spring has just sprung, more salmon will soon be gathering. And that means more arrests and probably more shootings. Police are advising that civilians give the dam a wide birth. This warning does not apply to certain tribal agencies, who will still be allowed to set up gill nets (to catch and eat salmon).

Now for the The Law of Unintended Consequences. Just like salmon, sea lions come in different models. Two of which are California sea lions and Stellar sea lions. California sea lions are a dime a dozen, but the Stellar sea lions, just like the chinook salmon, are blessed with the protection of the Endangered Species Act, and they are just as hungry as their cousins.

What to do? One Endangered Species is eating another Endangered Species. You can’t shoot the sea lions, and you also can’t not let them eat the salmon, but then you can’t let the salmon be eaten.

What a predicament!

(Note: I do not have an iPhone, nor “smart” phone of any kind.)

Addiction is a strong word. It means, as the authors of the study are content to allow us to infer, an abnormal craving or desire, even to the point of pathology. Drunks and druggies are addicts. Obsession is a close cousin to addiction.

It’s possible to be clever about this. We are all “addicted” to food, air, water. And the same with sleep, talking, and so forth. These are “healthy” addictions. But only an academic would use that word to describe normal and necessary behaviors. The rest of us take it to mean something nasty and undesirable.

Especially if we hear that word used in a “scientific study” on behavior.

Professor Tanya Luhrmann—a Stanford anthropology professor whose early work looked into modern-day witchcraft, and who wrote what appears to be a useful book contrasting talk versus chemical therapies in psychiatry—decided to investigate “addiction” to iPhones.

Since Luhrmann has expertise in psychiatry, it is strange she would use such a strong word to describe how people use their cell phones. But she did: she directed a team which conducted a survey designed to diagnose the intensity of iPhone use.

And like many professors, Luhrmann used a ready sampling method: her students. How many? 200! Yes, only two hundred. From these 200, she, like a legion of academics before her, and host still to come, extrapolated to the rest of us.

According to a Live Science report on the study, students were “asked to rank their dependence on the iPhone on a scale of one to five — five being addicted and one being not at all addicted — 10 percent of the students acknowledged full addiction to the device, 34 percent ranked themselves as a four on the scale, and only 6 percent said they weren’t addicted at all.”

From this, they inferred the title, “iPhone Addictive, Survey Reveals.”

In other words, students were allowed to self-define “addiction” and to rate themselves on their own self-defined scale. It is crucial to understand this. Imagine a group in which there is no pathological addiction, but then ask the members of that group to rate their “addiction” on a one to five scale. You will not see everybody answering “one”, or “no addiction.” This is especially true with students and their known sense of frivolity.

You can get a useful answer from a survey like this—from a group where it is known that no addictions exist—but the usefulness is constrained to describing the distribution of how people in that context, and in that survey, interpret the word “addiction.”

I would wager that College students will be more careless, more flippant, about using the word “addiction” than would, say, middle-aged executives. “Dude, I am totally addicted to this thing” is a sentence you can readily imagine an 18-year-old uttering. It’s more difficult to conjure an image of the assistant manager at Walmart saying the same thing. This is true even if both parties use their phones for identical lengths of time.

Now, in the 200 kids—excuse me, 200 mostly young adults—surveyed, there may have been genuine iPhone addicts, where that word is used in its pathological sense. But it is not a given that these true addicts would admit that they are in a survey. Sometimes the last person to know a man is a drunk is the drunk himself.

This is the failing of self-reported surveys. The possibilities for error are enormous. But these uncertainties are never—I have never seen an instance—accounted for in the final analysis. They sure weren’t in this study.

What probably happened is that Luhrmann and her colleagues decided it would be easy to pump out a quick paper and they cast about for something topical. Anything with “iPhone” in the title would sure be catchy. So, a survey was cobbled together—excuse me, an “instrument was designed”—and quickly given to two sections of Anthropology 101 (yes, I’m guessing about that).

This summary may be unfair, but how else could she and her colleagues have reported that “8 percent admitted that they have at some time thought ‘My iPod is jealous of my iPhone.’”? Eight percent weren’t volunteering that information: they were checking a box where that punchline already existed. Frivolity is not just a symptom of studenthood.

Why grouse, since it’s all in fun? Because not everybody will get the joke. And besides, it wasn’t meant to be funny. Luhrmann does a tremendous disservice by allowing a false—or at least far from proven—belief about addiction to cell phones to enter the culture.

It’s too easy to imagine some ambulance chaser citing this study in his attempt to win an award for “pain and suffering” for a client who was “so addicted” to her iPhone that she crashed and destroyed her car.

Update Intrepid legman, reader and contributer Bernie wrote and received a copy of the survey from Professor Luhrmann. It was the result of a graduate Research Methods class at Stanford. It looks it, too. The only thing I would change above is not how Professor Luhrmann could publish this—for it doesn’t look like she has or intends to—but how she could let it get out into the press, which was given the impression this was a respectable study. I still question how the word “addiction” is used.

A female named Harriet Harman at England’s Equalities and Human Rights Commission has discovered a new method of producing inequality under the law.

She has codified this method—England’s version of a legal patent—in a Parliamentary procedure called an “Equality Bill.” England, like many European countries, has proudly led the way in producing unequal treatment under the law for its citizens in the name of “Equality.”

The law mandates that organizations consider the “impact” of their policies on “minority” groups. This method of inequality was, of course, already well known. Harman’s genius was to insist that any group which holds distinct beliefs can form their own minority. To quote: “A belief need not include faith or worship of a god or gods, but must affect how a person lives their life or perceives the world.”

Harman gave “vegans” as an example of a new minority. A “vegan”, she said, is a person who “eschews the exploitation of animals for food, clothing, accessories or any other purpose and does so out of an ethical commitment to animal welfare.”

Lest you feel this is arbitrary, the Daily Mail reports that “[a] spokesman from the commission explained: ‘This is about someone for whom being vegan or vegetarian is central to who they are. This is not something ‘thought up by the commission’.”

Incidentally, it should be pointed out that no human can be a complete vegan in practice. It is impossible for a human to eat without both directly and indirectly killing animals. Many small animals—insects, worms, mice, and so forth—are killed both in the planting and harvesting of the vegetables vegans eat. The fields also provide a food source for many animals, and they in turn provide a food source for various predators. Thus, the very act of planting a crop causes the death of many animals.

In any case, the classification of minorities covers “any religious belief or philosophical belief” or and even “a lack of belief.”

Among the many anticipated successes of this new law will be the banning of dress codes (which might discriminate against transsexuals) and the outlawing of “ladies’ nights” at pubs.

That’s the news, now the analysis.

A minority group is defined as a set of humans who share a belief that is “central to who they are.”

Now, a set—in mathematics, anyway—can include just one, or even no, members. In the name of “equality”, then, England must admit that any belief, even if it is held by just one person, as long as that belief is “central to who they are.”

Technically, this must also be true for a belief that is (currently) held by nobody. I won’t insist on this definition, but examples of easily frightened bureaucrats anticipating protected beliefs are too easily brought to mind.

The law will require that organizations consider how their policies might adversely “harm” minorities. The law doesn’t say “harm”, but that, of course, is what it will amount to in practice. No minority sues for redress against advantages.

Inequality before the law arises because it is the minorities that get to define the “harm” they are caused. It is they who get to decide what actions are considered negative and discriminatory. This follows logically. It cannot be, for instance, the bureaucrat who codifies what counts as harm (for minorities consisting of at least one member), because it is the minorities who define themselves.

Thus, to be consistent, England must allow any individual to claim minority status. She must also allow each individual to define what he considers harmful behavior directed toward himself. And England must write regulations to disallow that harm, whatever it might be. Thus, it must guarantee unequal treatment under the law.

This is obviously insane.

There must have been some lone soul at the Equality Commission who realized this, because he put out that “scientific or political beliefs such as Marxism and fascism would not be covered.”

“Whew!” thought opponents, “At least we won’t have renegade bands of Hitlarians or other militant socialists running around. They’ll be disallowed to claim minority status.”

Maybe so. But if that’s true, the Commission is saying that it knows certain beliefs are verboten, which is equivalent to saying it knows which beliefs should be encouraged. That is, the Commission is saying it has a complete moral code, and that its moral code trumps any other, including those codes that are completely independent of government, such as religious codes.

This is a point of elementary logic. If the Commission can create a minority, it is they who are making the moral judgment of what is good and what bad. Either they must let every behavior claim equal status. Or they must discriminate.

Thus, the Orwell-named Equality Commission guarantees inequality. Since equality is nowhere to be desired, this is fine. But the evidence is that the Commission will discriminate against anything that is traditional in favor of whatever is not. And that is silly.

There are some new statistical papers floating around that conclude that the more intelligent among us tend to be atheists.

An equivalent, but more enjoyable, way of stating this is that dumber people tend to be theists. It must be fun for degree-holding atheist journalists to report these matters, since it flatters their degree-bred sense of superiority.

Which doesn’t follow. That “superiority”, I mean. It would if it were true that atheism is morally superior to theism. But morality is logically independent of intelligence (empirically, the evidence goes both ways; and since 1789, intellectuals have little to boast of, morally).

Point is, any study, or any reporting on such a study, that seeks to correlate intelligence and theism should remain mute on the subject of morality. But that’s not the case in the reporting and comments on our two articles (here and here)

The first study was authored by Satoshi Kanazawa, an “evolutionary psychologist.” Evolutionary psychologist spend a lot of telling us what we already knew (women hate philandering mates) or by telling us things that are false or misleading. Such as this statement by James Bailey, who said, “The adoption of some evolutionarily novel ideas [like atheism] makes some sense in terms of moving the species forward.”

Bailey also says that it’s the more intelligent that usher in novel ideas. This is unhelpful because, while it is true that every advance by definition requires a “novel idea”, every setback does, too. And since setbacks are more common than advances (is atheism a setback?), are intellectuals, on average, an evolutionary disadvantage? Maybe: see below.

Anyway, Kanazawa thinks atheism is a novel idea, and says that higher IQ people tend to support it. But Kanazawa’s study employs poor statistical methods. Here’s the problem.

Many do not come to atheism by reasoned thought about the existence or not of God. Most people do not engage theologians about, say, the strengths and weaknesses of the ontological argument.

As acknowledged in our second study by professor David Voas, they come to it through culture,. Fresh college students meet not-so-fresh students and stale professors who share a common belief that theism is stupid, and that belief comes from the blind following of tradition. Most new students, as is human nature, adopt this belief of their associates and superiors. To say it another way, they begin to blindly follow a different tradition.

But, since it is higher IQ kids who attend college and who are exposed to the culture of atheism, it makes it more likely that students, rather than non-students, who will become atheists. Atheism and IQ will show a positive correlation, but what is missing is the causation. There will also be a correlation between “degree of liberalism” and IQ, which Kanazawa also tracked, and for the same reason.

If you object to that, it is probably because you have forgotten that for most of history people with high IQs were theists, and that it was those with the highest IQs who contributed the most to theology. Arguments for or against the existence of God have not changed much through time, but culture has. It is thus more plausible that culture and not intellect is what drives belief.

Kanazawa is not silent on causation. He says that theism causes “paranoia.” He strung these English words together, “It helps life to be paranoid, and because humans are paranoid, they become more religious, and they see the hands of God everywhere.” Each of those words is English, but their ordering is gibberish.

Is he implying that theists are mentally ill and atheists not? Kanazawa would not be the first to argue that theists are insane, but he may be the first who attached a p-value to that belief. Or is he merely saying that humans are cautious because the future is uncertain? No, because he can’t resist the disparaging, and false, remark that theists “see the hands of God everywhere.”

Bailey takes a subtler view. He claims that, regardless whether a novel idea is good or bad, holders of novel beliefs, who tend to be smarter, attract more mates. His argument is thus a version of the theory that some women like bad boys. There is no proof of his theory, of course, and it is difficult to test because it is difficult to quantitatively define “novelty.” For one, ideas do not have to be liberal to be novel, even if the predominant culture is conservative.

Even Kanazawa himself is aware that his own argument is on thin ice. For example, he acknowledges that nowadays “[m]ore intelligent people don’t have more children.” This is true.

So I wonder: does he realize that this empirical truth negates everything else in his study?

This is satire.¹

Hi, I’m scientist Paul Ehrlich. You might remember me from such failed predictions as “Everybody’s Going to Die by 1984!” and “Tin to Become More Precious than Water!”

Today, I’m going to foretell you another story of coming calamity. This story is about our climate, and how if something isn’t done right now, or even yesterday, we will all, once more, be consigned to cataclysm.

But this time we have a chance! Because this time, we know how to fight back. We know who are enemies are, and we know how to hit ‘em where it hurts.

Before I tell you about that, let me take you on a journey that started in 1968.

It was a time of free experimentation. A time of rebellion, happiness, and unbridled liberty. Everywhere the young were throwing off the shackles of the old. Mankind—and womankind—was growing up.

This wasn’t just happening in popular culture; no, sir! This went on in science, too. In labs all across the country, men—and women—were tossing off their restrictive lab coats and engaging in wild speculation!

And I was in on it. I can proudly say that I led the way. Is was I who began to understand that soon—yes, very, very soon—hundreds of millions of people were going to die. Not millions, not tens of millions, but hundreds of millions!

Why? Because they were going to run out of food. They were going to—I still get the shivers thinking of this—starve to death.

Well, I was off a little in that one. But in my favor, since my book appeared, there have been multiple reports of children being sent to bed without their dinner. Hey, there was actual starvation, too! Though most of it was caused by wars or because of failed socialist central planning. Still, a dead body is a dead body, and each one counts in my favor.

Anyway, if you only look only at the starvation numbers, you’ll miss the frightening fact that population sure did increase since 1968! That has to count for something, right? It’s true I used the term “Population Bomb”, but I didn’t mean that population was going to “bomb out.” I merely meant that population was going to increase. So you can see that I was right after all!

I suppose it’s true nowadays that demographers are worried about underpopulation, particularly in advanced societies. But how many demographers do you know that have a world-wide following like me? How many have received a MacArthur Foundation Genius Award like I have? Since nobody listens to these demographers, but they do listen to me, it can only mean that I know what I’m talking about.

Not convinced? Why, even now I have President Obama’s ear—once removed, through my old, and devoted, student John Holdren. He’s Obama’s Science Adviser. And since he knows what I know—like how badly we need a global redistribution of wealth, and how some women should undergo compulsory abortion—you can bet the President is getting my top-notch advice.

If you concentrate on those missed predictions to dismiss me, you will be making a terrific mistake. Because it’s not the predictions that count, it’s how important the theory behind them is! And no theory is more important than man-made—and woman-made—adverse climate change.

It’s true, I’m not a climate scientist, but I like what those guys are up to. The fundamental basis of their theory is that the world’s weather woes are caused by our greed and excess breeding. That has to be right. I sure want it to be right. Therefore, it must be.

My friends, you have heard that there is a highly organized cabal of skeptics—fattened by the money of oil companies!²—who have the temerity to publicly dispute climate change. These people will not admit that the science is undisputed. This cannot stand.

And it won’t. This time we’re going to stop those meddling skeptics by unleashing a tsunami of pain. How? We’re going to take out an ad in the New York Times!

When those deniers wake up on the morning of the ad placement and have their papers read to them, boy! Will they be in for a gut-busting surprise? You bet. Opposition to our wholly beneficent plans will fold faster than a coward holding a straight flush.

Listen, friends. If we don’t do something now, then everybody’s going to die. No, not everybody, but most everybody. Maybe even me. You already know that my predictions about this kind of thing were technically right before, so you know I have a good chance of being technically right again.

Help by sending money to my organization today. Good day, and thank you for listening.

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¹I weep that I must have this disclaimer.

²I received no money, nor any other consideration, for this article. Or for any other that I have ever wrote about climatology. In fact, the opposite is true. All work I do comes out of my own pocket.

Thanks to an anonymous reader for help with the research.

Thanks to readers Ari Schwartz and Tom Pollard for suggesting this article.

Take any two sets of numbers, where the only restriction is that a reasonable chunk inside each set has to be different than one another. That is, we don’t want all the numbers inside a set to be equal to one another. We also want the sets to be, more or less, different, though it’s fine to have some matches. Make sure there is a least a dozen or two numbers in each set: for ease, each set should be the same size.

You could collect numbers like this in under two minutes. Just note the calories in an “Serving size” for a dozen different packages of food in your cupboard. That’s the first set. For the second, I don’t know, write down the total page counts from a dozen books (don’t count these! just look at the last page number and write that down).

All set? Type these into any spreadsheet in two columns. Label the first column “Outcome” and label the second column “Theory.” It doesn’t matter which is which. If you’re too lazy to go to the cupboard, just type a jumble of numbers by placing your fingers over the number keys and closing your eyes: however, this will make trouble for you later.

The next step is trickier, but painless for anybody who has had at least one course in “Applied Statistics.” You have to migrate your data from that spreadsheet so that it’s inside some statistical software. Any software will do.

OK so far? You now have to model “Outcome” as a function of “Theory.” Try linear regression first. What you’re after is a small p-value (less than the publishable 0.05) for the coefficient on “Theory.” Don’t worry if this doesn’t make sense to you, or if you don’t understand regression. All software is set up to flag small p-values.

If you find one—a small p-value, that is—then begin to write your scientific paper. It will be titled “Theory is associated with Outcome.” But you have to substitute “Theory” and “Outcome” with suitable scientific-sounding names based on the numbers you observed. The advantage of going to the cupboard instead of just typing numbers is now obvious.

For our example, “Outcome” is easy: “Calorie content”, but “Theory” is harder. How about “Literary attention span”? Longer books, after all, require a longer attention span.

Thus, if you find a publishable p-value, your title will read “Literary attention span is associated with diet”. If you know more about regression and can read the coefficient on “Theory”, then you might be cleverer and entitle your piece, “Lower literary attention spans associated with high caloric diets.” (It might be “Higher” attention spans if the regression coefficient is positive.)

That sounds plausible, does it not? It’s suitably scolding, too, just as we like our medical papers to be. We don’t want to hear about how gene X’s activity is modified in the presence of protein Y, we want admonishment! And we can deliver it with my method.

If you find a small p-value, all you have to do is to think up a Just-So story based on the numbers you have collected, and academic success is guaranteed. After your article is published, write a grant to explore the “issue” more deeply. For example, we haven’t even begun to look for racial disparities (the latest fad) in literary and body heft. You’re on your way!

But that only works if you find a small p-value. What if you don’t? Do not despair! Just because you didn’t find one with regression, does not mean you can’t find one in another way. The beauty of classical statistics is that it was designed to produce success. You can find a small, publishable p-value in any set of data using ingenuity and elbow grease.

For a start, who said we had to use linear regression? Try a non-parametric test like the Mann-Whitney or Wilcoxon, or some other permutation test. Try non-linear regression like a neural net. Try MARS or some other kind of smoothing. There are dozens of tests available.

If none of those work, then try dichotomizing your data. Start with “Theory”: call all the page counts larger than some number “large”, and all those smaller “small.” Then go back and re-try all the tests you tried before. If that still doesn’t give satisfaction, un-dichotomize “Theory” and dichotomize “Outcome” in the same way. Now, a whole new world of classification methods awaits! There’s logistic regression, quadratic discrimination, and on and on and on… And I haven’t even told you about adding more numbers or adding more columns! Those tricks guarantee small p-values.

In short, if you do not find a publishable p-value with your set of data, then you just aren’t trying hard enough.

Don’t believe just me. Here’s an article over at Ars Technica called “We’re so good at medical studies that most of them are wrong” that says the same thing. A statistician named Moolgavkar said “that two models can be fed an identical dataset, and still produce a different answer.”

The article says, “Moolgavkar also made a forceful argument that journal editors and reviewers needed to hold studies to a minimal standard of biological plausibility.” That’s a good start, but if we’re clever in our wording, we could convince an editor that a book length and calories correlation is biologically plausible.

The real solution? As always, prediction and replication. About which, we can talk another time.