William M. Briggs, Statistician

Michael Crichton, as you will have heard by now, is dead. Unfortunately.

The Wall Street Journal today reprinted an excerpt of a speech Crichton gave called “Aliens Cause Global Warming.” Regular readers of this blog will know Crichton’s opinion on the certainty of man-made catastrophic climate change. Just a reminder (from his speech):

No longer are [climate] models judged by how well they reproduce data from the real world — increasingly, models provide the data. As if they were themselves a reality. And indeed they are, when we are projecting forward. There can be no observational data about the year 2100. There are only model runs.

This fascination with computer models is something I understand very well. Richard Feynman called it a disease. I fear he is right. Because only if you spend a lot of time looking at a computer screen can you arrive at the complex point where the global warming debate now stands.

Nobody believes a weather prediction twelve hours ahead. Now we’re asked to believe a prediction that goes out 100 years into the future? And make financial investments based on that prediction? Has everybody lost their minds?

To explain why he was flummoxed, Crichton first made a point about SETI, the Search for Extraterrestrial Intelligence. A lot of people in that field make reference to the Drake Equation, originated by SETI big cheese Frank Drake. That equation is

• N = R ^* x f_p x n_e x f_l x f_i x f X L

.

We want to solve for N, which is the number of civilizations in our galaxy with which intelligent communication is possible. N depends on the rate of star formation R ^*, the fraction f_p of those stars that have planets, and all those other things you can look up.

Crichton says:

This serious-looking equation gave SETI a serious footing as a legitimate intellectual inquiry. The problem, of course, is that none of the terms can be known, and most cannot even be estimated. The only way to work the equation is to fill in with guesses. And guesses — just so we’re clear — are merely expressions of prejudice. Nor can there be “informed guesses.” If you need to state how many planets with life choose to communicate, there is simply no way to make an informed guess. It’s simply prejudice.

The Drake equation can have any value from “billions and billions” to zero. An expression that can mean anything means nothing. Speaking precisely, the Drake equation is literally meaningless, and has nothing to do with science. I take the hard view that science involves the creation of testable hypotheses. The Drake equation cannot be tested and therefore SETI is not science. SETI is unquestionably a religion.

The fact that the Drake equation was not greeted with screams of outrage — similar to the screams of outrage that greet each Creationist new claim, for example — meant that now there was a crack in the door, a loosening of the definition of what constituted legitimate scientific procedure. And soon enough, pernicious garbage began to squeeze through the cracks.

I agree with him that none of these terms can be known exactly, or even sufficiently precisely to calculate a quantitative answer for N. I also agree that the pursuit of N can take on religious qualities.

But I can’t agree that SETI itself is worthless, nor can I agree that interest in it loosens the definition of “legitimate scientific procedure.” SETI is not just the Drake equation.

Now, I will not attempt to defend even one procedure that SETI workers use, nor will I comment on any statement made by any of its proponents. I cannot say, for example, that searching nearby stars for signals in the hydrogen line makes any sense. But I will say SETI is not the same as religion

I am interested in saying something about the probability of this proposition:

    S = “Intelligent/sentient life besides that on planet Earth exists”

Because we must calculate the probability of S is conditional on some evidence, I offer this blog. Yes, because this blog—because you and I—exist, it means that the universe is set up to allow at least one species of sentient life. Therefore, it is rational to believe that the probability of S given this evidence is greater than 0. I have no idea how much larger than 0 it is. If you are a fan of the reasoning behind the Fermi Paradox, you might say that the probability, while non-zero, is trivially small.

The Fermi Paradox basically says that, since the universe is about 10-13 billion years old, and the one sentient-life example we know of only took about 4-5 billion years to evolve, and since there are plenty of stars and galaxies, there should be sentient life all over the place. That is, SETI should be easy, and since it isn’t, since we haven’t made contact yet, this implies that we are the first or only sentient species. There are obvious subtleties to each stage of that argument that I glossed over, but that’s the gist.

The Fermi Paradox is also conditional on information not articulated. One obvious item is the proposition that all sufficiently advanced civilizations would want to make contact with us. Not just with other species, but with us. That’s a mighty big supposiion. Another hidden assumption is that we ourselves are sufficiently advanced enough to detect messages aimed at us, or have the ability to intercept messages meant for other beings. Pretty big guess, especially with the knowledge that the more efficient a message gets, the more it looks to an outside like noise (basic information theory; deep ties with probability and statistics there), and so civilizations more advanced than us might have communications which are impenetrable to us.

That argument cuts both ways, of course. If the messages are too complex, any search for them is fruitless. And, well, you get the idea. It’s complicated, so much so that it is not an open and shut judgment that SETI is valueless.

Though we have to be careful. Wishcasting is always a danger here, as everywhere. A lot of people—me included—want S to be true and this naturally clouds our judgment.

A few months ago we looked at a paper that purported to show that watching TV causes autism. Well, that paper has finally been peer reviewed, and therefore published. It’s making the rounds in the media on this historically slow news day.

Monthly Weather Review chief editor Dave Schultz found this article on the BBC web site. Climate-computer guy Dan Hughes found another at the Washington Post.

The original draft paper is here. If you are in one of the ivory towers, you can download the paper here, at the Archives of Pediatrics & Adolescent Medicine.

The idea is that when it rains it drives kids inside to watch television and that watching TV—never mind how—induces autism. The more hours kids spend in front of the boob-tube, the more cases of autism. Since you can’t measure the number of hours of watching TV, you have to do something else. The authors decided that precipitation would be a good proxy. Here are some of the comments from July:

But how can you tell how much TV all these kids watched? You can’t. There is no way to go back to 1970 and count how many hours each baby watched TV. This is a dilemma, because we would really like to test the dose-response. Perhaps there is a proxy? A proxy is a stand-in variable that is so strongly associated with hours of TV watched that it’s almost as good as the real thing. Can you think of any?

How about precipitation? Sure, rain and snow. After all, when it rains, what else is there to do but watch TV? Actually, lots, and when it snows, there’s even more. But, this is the proxy chosen by the researchers (their Figure 6 will hold some interest for those interested in global warming).

They plotted up maps by county for California, Oregon, and Washington, and colored in counties that had more than median precipitation (from 1990-2001) and then colored those with higher than median autism rates. These colored squares tended to be in the same spot, and is what led them to the conclusion that watching TV causes autism. Case closed.

Mark Lever, chief executive of The National Autistic Society, is properly sanguine about the research. He said, “the latest theory would join a succession of others advanced about the condition and its origins.”

In recent years autism has been linked to factors as varied as older aged fathers, early television viewing, vaccines, food allergies, heavy metal poisoning, and wireless technology, to name just a few.

Some of these theories are little more than conjecture or have been discredited, others seem more promising and are in need of further study. As yet, however, very few have been substantiated by scientific research.

We don’t yet understand what causes autism, although scientists do believe that genetic factors might play a part.

People with autism and their families are naturally concerned to get the right information and there is a lot of confusion and concern over the conflicting theories put forward.

Another guy named Weiss “thinks the results of the study need to be taken with a grain of salt.” To counter that, a man called Lathe said, “Emissions from manufacturing industries, power plants, and from domestic waste incineration generally rise to the troposphere to be diluted into the large volume of the atmosphere. Precipitation can dump this load back on the land, to be absorbed by plants and animals in the food chain.” Not very good meteorology there, because we could equally say that lack of precipitation allows the atmospheric pollution to be worse, causing increases in inhaled ozone, etc. etc.

Overall, there doesn’t seem to be a solid link between rain or TV and autism. The authors of the paper even say “that families more prone to having autistic children may reside in areas with high levels of precipitation, or that such areas might use broader diagnostic criteria for diagnosing autism.” There does seem to have been an increase in the rates of autism, but that increase could very easily be from increased awareness and subsequent diagnoses of the disease.

Nothing has changed between the draft work and the peer-reviewed one to cause me to change my mind about the value of the paper. What I didn’t know before, but I learned from the Post today, is that the lead author and economist Waldman has a son who is autistic. I can therefore understand what motivates him and the desire to find out what happened.

Those are the words of Warren Buffet, who warned of the coming credit crisis. Buffet—one of the very few—had little faith in the “complicated, computer-drive models systems that many financial giants relay on to minimize risk.”

Reader Dan Hughes reminds us of this article in today’s Wall Street Journal, which looks at why AIG did so miserably.

AIG built a lot of models which attempted to quantify risk and uncertainty in their financial instruments. They, like many other firms, tried to verify how well these models did, but they only did so on the very data that was used to build the models.

Now, if you are a regular reader of this blog, you will know that we often talk about how easy it is to build a model to fit any set of data. In fact, with today’s computing power, doing so is only a matter of investing a small amount of time.

But while a model fitting the data that was used to build it is necessary condition for that model to work in reality, it is not a sufficient condition. Any model must also be tried on data that was not used—in any way—to build it.

What happened at AIG, and at other financial houses, was that events occurred which were not anticipated or that had not happened before. Meaning, in short, that the models in which so many had so much faith, did not work in reality.

There is only one true measure of a model’s value: whether or not it works. That it is theoretically sound, or that it uses pleasantly arcane and inaccessible mathematics, or that it matches our desires, or that “only PhDs can understand” it are all very nice things, but they are none of them necessary. Many complex models which are in use are loved and trusted because of these things, but they should not be. They should only be valued to the extent that they accurately quantify the uncertainty of the real-life stuff that happens (climate models anyone?).

What the AIG models failed to account for were the “unknown unknowns”—to use Donald Rumsfeld’s much maligned quotation. They did not quantify the uncertainty of events which they did not know about. They thought that the models quantified the uncertainty of every possible thing that would happen, but of course they did not. Meaning that they were overconfident.

AIG’s failure is yet another in a long series of lessons that the more complex the situation, the less certain we should be.

(A subscription is required to read the full WSJ article.)

The book is The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives by Deirdre Nansen McCloskey and Steve Ziliak.

From the description at Amazon:

The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesn’t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots.

This is part of the theme I’ve long been pushing. McCloskey and Steve Ziliak are shocked, perplexed, and bewildered that classical statistics and p-values are still being used.

I’m not so shocked. They want people to abandon p-values and start using effect sizes. A fine first step, but one that doesn’t solve the whole problem.

I say we should drop p-values like Obama dropped Rev. Wright, eschew effect sizes like Joe Biden did reality, and return to observables. Let me, as they say, illustrate with a (condensed) example from by book.

Suppose there are two advertising campaigns A and B for widget sales. Since we don’t know how many sales will happen under A or B, we quantify our uncertainty in this number using a probability distribution. We’ll use a normal, since everybody else does, but the example works for any probability distribution.

Now, a normal distribution requires two unobservable numbers, called parameters, to be specified so that you can use it. The names of these two parameters are μ and σ. Both ad campaigns need their own, so we have μ_A and σ_A, and μ_B and σ_B. Current practice more or less ignore the σ_A and σ_B, so we will too.

Here is what “statistical significance” is all about.

Actual sales data under the two campaigns A and B is taken. A statistic is calculated: Call it T. It is a function of differences in the observed sales under both campaign. Never mind how it’s calculated. T is not unique, and for any problem dozens are available. With T in hand, the classical statistician makes this mathematical statement:

   μ_A=μ_B

and then the infamous p-value is calculated, which is

   Probability(Another T > Our T given that μ_A=μ_B)

where the “Another T” is the statistic we would get if we were to repeat the entire experiment again. Do we repeat it again? No, so we are already in deep waters. But never mind.

If the p-value is less than the magic number of 0.05, then the results are said to be statistically significant.

Quick readers will have spotted the major difficulty. What does equating two unobservable parameters in order to calculate some weird probability have to do with whether the campaigns are different than one another?

The words are not much, which is why McCloskey and Ziliak call the dependence on p-values a cult.

They recommend, in its place, estimating the effect size, which is this:

   μ_A - μ_B.

Eh. It’s part way there, but it’s still a statement about unobservable parameters (and it still ignores the other unobservable parameters σ_A and σ_B).

What people really want to know is this:

   Probability(Sales A > Sales B given old data).

Or they’d like to estimate the actual sales under A or B. There are new ways that can calculate these actual probabilities of interest. However, you won’t learn these methods in any but the most esoteric statistics class.

And that is what should change.

Because, I am here to tell you, you can have a p-value as small as you like, you can have an effect size as big as you like, but it can still be the case that

   Probability(Sales A > Sales B given old data) ~ 50%!

which is the same as just guessing. Yes, the actual, observable numbers, the real-life stuff, the physical, measurable, tangible decisionable reality can be no different at all. At least, we might not be able to tell they are any different.

And that’s the point. The old ways of doing things were set up to make things too certain.

I wouldn’t go so far as to say reliance on the old ways was cultish. Most people just don’t know of the alternatives.

Take a look at his picture:

This is from an article by “The Numbers Guy” Carl Bialik at the Wall Street Journal. The story is about how life expectancy calculators are not terribly accurate. This really isn’t much of a surprise, but the picture should be.

This is because both presidential candidates, and of course many other people, nervously claim that there is a “Health Care Crisis! We have to do something!”

Yes, it’s so bad that the people are living longer and longer and longer… This picture says that whatever the crisis is, it clearly doesn’t have to do with that part of health that keeps people alive. I would argue that that part is the most important; apparently, others disagree.

This is another example of the phenomenon that the better things get, the more people complain. Or maybe people don’t complain more, they complain at the same rate, but because things are better, the complaints are about matters and points that are increasingly trivial.

Hasn’t somebody given a name for this dynamic?

I use the word “random” in the sense that you did not know what topics I would select today. And I use the word know in its logical sense.

On Polling

From Instapundit comes this link to the WizBang blog on polls and polling.

Mr Wiz Bang seeks to reassure his readers that the picture is not as bleak for McCain viz. the polls as reported in the media.

All of the obvious suspects are here. The polls are commissioned and designed by folks who have a definite stake and desire in the outcome of the election. This of course does not prove that the polls are biased, but it should increase the probability that you think so.

The ordering of the questions and the exact questions used are seldom revealed, but are of obvious importance. For example, in one poll Mr WB discovered that questions about McCain came right after people are solicited for their opinion on President Bush.

You never hear about non-response. For example, pollsters ask 100 people, or try to ask 100 people, but only 20 respond. Who? Why? Are these non-responses correlated with the outcome? Usually, and in fact especially in politics, they are.

One thing Mr WB doesn’t mention is lying. People lie like dogs on surveys and polls. Sometimes, the lying is evenly spread out on both sides of Yes and No, but sometimes not. In this election, I suspect the lying is not even.

One place I did not know about is the National Council on Public Polling, a body whose purpose, inter alia¹, is to provide ethical guidelines on polling. If you have ever found yourself caring about any poll, then you ought to read their “20 Questions A Journalist Should Ask About Poll Results.”

I won’t bore anybody with the technicalities, but “11. What is the sampling error for the poll results?” is based on classical statistics, and thus the typical “+/- 4 points error” you hear is wrong and should, as an extremely crude rule of thumb, be multiplied by 2. This fudge factor accounts for uncertainty in the true error, not the statistical formula error, which nobody ever really cares about. The true error is this: A poll says, 46% support M, and, in the end, actual voting reveals 74% support for M, then the error is 46% - 58% = -12%.

Their take on “18. What about exit polls?” also does not account for lying. I’ve told this story 100 times, but it bears repeating. John Kerry’s exit polls had him winning, in Manhattan, by about 10 to 1. The actual result was Kerry winning by about 5 to 2. Now, it’s true that Kerry still won the city, but the actual result wasn’t even close to that predicted by the poll. People who live in Manhattan are under a lot of pressure to voice support for Democrats.

Suicides and economic downturns

This idea comes from Dave Schultz, intrepid Chief Editor of Monthly Weather Review (I am one of the many Associate Editors there; Dave, unsolicited, was kind enough to put a link to my book on his page).

Dave pointed to this article from a local New York City paper. It’s a story of how “researchers” continually find surprising and suspicious correlations with economic data.

You might have heard this one in the news last week. A “researcher” named Pettijohn supposedly found that in lean economic times, chubbier models were featured in Playboy magazine. To which I can only say: isn’t tenure a wonderful thing?

Undoubtedly still drooling—I mean reeling—from that stunning finding, Pettijohn went on to discover “that in uncertain times, people tend to prefer songs that are longer, slower, with more meaningful themes.” Which I guess explains how Barry Manilow got to be popular (From Barry: “You get what you get when you go for it”).

As insightful as Pettijohn is, he doesn’t hold a box of tissues to Leo J. Shapiro, chief executive of SAGE, a Chicago-based consulting firm. Says Shapiro: “DURING a recession, laxatives go up, because people are under tremendous stress, and holding themselves back.”

Now that’s research. “Bob, this recession measures a solid—and I do mean solid—7.4 on the old sphinctometer.”

A guy named Ruhm says that suicides increase when dollars decrease. But the data he uses (they picture it) has already been massaged and filtered etc. and we all know what happens when you smooth time series and then use those smoothed series as inputs to other analyses, right?

_____________________________________
¹This phrase was a favorite of my intellectual grandfather, Allan Murphy. Murphy was huge in forecast verification and meteorological statistics, a love which he passed on to Dan Wilks (the mustache is real), who is half my father. Meaning: Murphy was Wilks’s advisor, and Wilks was, in part, mine.

The way peer review works is broken, according to a new finding by John Ioannidis and colleagues in their article “Why Current Publication Practices May Distort Science”. The authors liken acceptance of papers in journals to winning bids in auctions: sometimes the winner pays too much and the results aren’t worth as much as everybody thinks.

What normally happens is that an author writes up an idea using the accepted third person prose, which includes liberal use of the royal we, as in “In this paper we prove…” His idea is not perfect, and might even be wrong, and he knows it. But he needs papers—academics need papers like celebrities need interviews with network news readers—and so he sends it in, hopeful.

Impact Factors

Depending on how good our author thinks his paper is, coupled with the size of his ego, he will choose a journal from a list ranked by quality. This rating is partly informal—word of mouth—and partly pseudo-statistical—”impact factors.” “Impact” factors are based on a formula of how many citations papers in the noted journal get. The idea is that the more citations a work gets, the better it is. This is, as you might easily guess, sometimes true, sometimes not.

“Gaming” of impact factors is explicit. Editors make estimates of likely citations for submitted articles to gauge their interest in publication. The citation game has created distinct hierarchical relationships among journals in different fields. In scientific fields with many citations, very few leading journals concentrate the top-cited work: in each of the seven large fields to which the life sciences are divided by ISI Essential Indicators (each including several hundreds of journals), six journals account for 68%–94% of the 100 most-cited articles in the last decade.”

One of the main advantages of the publish and perish model of academic careerism has been the explosive growth of journals. In the field of mathematical statistics, for example, we have JASA and The Annals, the Cadillac and BMW of journals, but we also have Communications in Statistics and the Far East Journal of Theoretical Statistics, the Pinto and Yugo of publications. As Ioannidis says, “Across the health and life sciences, the number of published articles in Scopus-indexed journals rose from 590,807 in 1997 to 883,853 in 2007, a modest 50% increase.” Similar increases can be found in every field.

Even though there is, as the common saying goes, a journal for every paper, many authors shoot for the best at first because, as the commercial says, “Hey, you never know.” Naturally, then, the better journals end of rejecting most of their submissions. What happens next partially highlights the auction analogy.

Journals closely track and advertise their low acceptance rates, equating these with rigorous review: “Nature has space to publish only 10% or so of the 170 papers submitted each week, hence its selection criteria are rigorous”—even though it admits that peer review has a secondary role: “the judgement about which papers will interest a broad readership is made by Nature’s editors, not its referees”. Science also equates “high standards of peer review and editorial quality” with the fact that “of the more than 12,000 top-notch scientific manuscripts that the journal sees each year, less than 8% are accepted for publication”.

“Elite” colleges and universities do much the same thing: encourage as many applications as necessary just so that they can lower their acceptance rates, that figure figuring high in the algorithm of Eliteness.

Publish or Perish

The auction analogy breaks down at this point because there are some many other outlets for publication. The top journals do end up with better papers because of at least three things: there are so many outlets that a natural ranking always results, the citation arms race, and because of the non-numerical prestige factor. It is true that just because a paper is in a top journal, it is no guarantee that its findings are correct and useful, but I would say that it increases the probability that they are correct and useful.

If you cannot find a journal to take your paper, no matter how atrocious it is, then you aren’t trying hard enough. Many journals’ entire reason for existence is to take in strays. Sending in dreck to a fourth-rate journal isn’t always irrational. Publish or perish is a real phenomenon, and very often those judging your “tenure package” do nothing more than count the papers. When I was at Weill-Cornell (Med School), I was told that the number was 20. Naturally, this number is unofficial and never written down, but everybody knows it. Your colleagues will, however, be aware which journals are bottom feeders. A friend of mine once said “I give 1 point for every JASA or Annals paper. And I subtract 2 for every Communications.”

Fads

Ioannidis and his co-authors missed one important auction analogy: Fads. I’m thinking of that “artist” who pickles sharks and other dead animals and calls it “art.” That guy recently had an auction selling his taxidermy and raked in millions from fools bigger than himself. Sooner, and probably later, people will return to their senses and no longer buy what this guy is selling.

The same thing happens in “science” publishing. Papers within a fad are given what amounts to a free pass and proliferate. There was a time, right after the discovery of x-rays for example, when there was a proliferation of new “ray” discovery papers. The most infamous is Blondlot’s N-rays. In the ’80s and ’90s in psychology, the fad was “recovered memories” and “satanic cult discovery.”

Once a fad starts, new fad-papers cite the old ones, papers appear at an accelerating rate, and an enormous web of “research” is quickly built. Seen from afar, the web looks solid. But peer closer and you can see how easily the web can be torn to shreds. Today’s fad is “The Evils That Will Befall Us Once Global Warming Hits.” An example of how ridiculous this fad has gotten is this paper, which purports to show how suicides will increase in Italy Once Global Warming Hits.

It is not clear, as it probably never is when in the midst of one, when this fad will peter out. In any case, there is more than auction frenzy and faddishness that explains why peer review is not perfect.

Bad Statistics

For example, the Italian global-warming suicide paper used statistics to “prove” its results. The statistical methods they used were so appalling that I am still recovering from my review of the paper. The frightening thing is that this paper was not an exception.

Ioannidis is well known for a paper he wrote a few years ago claiming that most published research (that used classical statistics methods) was wrong. He said (quote from the auction paper)

An empirical evaluation of the 49 most-cited papers on the effectiveness of medical interventions, published in highly visible journals in 1990–2004, showed that a quarter of the randomised trials and five of six non-randomised studies had already been contradicted or found to have been exaggerated by 2005…More alarming is the general paucity in the literature of negative data. In some fields, almost all published studies show formally significant results so that statistical significance no longer appears discriminating. [emphasis mine]

Regular readers of this blog will recognize the sentiments. The simple fact is that if you use classical statistics methods—or even a lot of Bayesian parameter-focused methods—the results will be too certain. That is, the methods might give a correct answer to a specific question, but nobody can remember what the proper question is and so they substitute a different one. The answer thus no longer lines up with the question, and people are misled and become too certain.

Just why this is so will have to wait for another day.

Here is a question I added to my chapter on logic today.

New York City “Health Czar” Thomas Frieden (D), who successfully banned smoking and trans fat in restaurants and who now wants to add salt to the list, said in an issue of Circulation: Cardiovascular Quality and Outcomes that “cardiovascular disease is the leading cause of death in the United States.” Describe why no government or no person, no matter the purity of their hearts, can ever eliminate the leading cause of death.

I’ll answer that in a moment. First, Frieden is engaged in yet another attempt by the government to increase control over your life. Their reasoning goes “You are not smart enough to avoid foods which we claim—without error—are bad for you. Therefore, we shall regulate or ban such foods and save you from making decisions for yourself. There are some choices you should not be allowed to make.”

The New York Sun reports on this in today’s paper (better click on that link fast, because today could be the last day of that paper).

“We’ve done some health education on salt, but the fact is that it’s in food and it’s almost impossible for someone to get it out,” Dr. Frieden said. “Really, this is something that requires an industry-wide response and preferably a national response.”…”Processed and restaurant foods account for 77% of salt consumption, so it is nearly impossible for consumers to greatly reduce their own salt intake,” they wrote. Similarly, regarding sugar, they wrote: “Reversing the increasing intake of sugar is central to limiting calories, but governments have not done enough to address this threat.”

Get that? It’s nearly impossible for “consumers” (they mean people) to regulate their own salt intake. “Consumers” are being duped and controlled by powers greater than themselves, they are being forced to eat more salt than they want. But, lo! There is salvation in building a larger government! If that isn’t a fair interpretation of the authors’ views, then I’ll (again) eat my hat.

The impetus for Frieden’s latest passion is noticing that salt (sodium) is correlated—but not perfectly predictive of, it should be emphasized—with cardiovascular disease, namely high blood pressure (HBP). This correlation makes physical sense, at least. However, because sodium is only correlated with HBP, it means that for some people average salt intake is harmless or even helpful (Samuel Mann, a physician at Cornell, even states this).

What is strange is that, even by Frieden’s own estimate (from the Circulation paper), the rate of hypertension in NYC is four percentage points lower than the rest of the nation! NYC is about 26%, the rest of you are at about 30% If these estimates are accurate, it means New York City residents are doing better than non residents. This would argue that we should mandate non-city companies should emulate the practices of restaurants and food processors that serve the city. It in no way follows that we should burden city businesses with more regulation.

Sanity check:

[E]xecutive vice president of the New York State Restaurant Association, Charles Hunt…said any efforts to limit salt consumption should take place at home, as only about 25% of meals are consumed outside the home.

“I’m concerned in that they have a tendency to try to blame all these health problems on restaurants…This nanny state that has been hinted about, or even partially created, where the government agencies start telling people what they should and shouldn’t eat, when they start telling restaurants they need to take on that role, we think its beyond the purview of government,” Mr. Hunt said.

Amen, Mr Hunt. It just goes to show you why creators and users of statistics have such a bad reputation. Even when the results are dead against you, it is still possible to claim what you want to claim. It’s even worse here, because it isn’t even clear what the results are. By that I mean, the statements made by Frieden and other physicians are much more certain than they should be given the results of his paper. Readers of this blog will not find that unusual.

What follows is a brief but technical description of the Circulation paper (and homework answer). Interested readers can click on. Continue Reading »

From Jerry Pournelle (What? You haven’t read Lucifer’s Hammer yet?) on how just about everybody making bets in the financial markets were wrong. This “everybody” includes very highly educated, extraordinarily well paid, respected, etc. etc., people.

One of my favorite lines, “Given incorrect models to work with, the computers continued to forecast profits right up to the crash.”

Another “As to what can be done, it may not matter. That is, it’s important what we do, but the chance that it will be done sanely and rationally is very small.” Of course, what we do will be pronounced as “the” thing to do. After all, the eventual plan, whatever it might be, will be made by experts.

Pournelle’s worry, as should be ours, is that the only thing that will happen is the creation of yet another big-government bureaucracy.

“Pournelle’s Iron Law of Bureaucracy states that in any bureaucratic organization there will be two kinds of people: those who work to further the actual goals of the organization, and those who work for the organization itself. Examples in education would be teachers who work and sacrifice to teach children, vs. union representative who work to protect any teacher including the most incompetent. The Iron Law states that in all cases, the second type of person will always gain control of the organization, and will always write the rules under which the organization functions.”

Ah, government bureaucracy. Is there anything experts at the government can’t fix? I know I can’t wait for the EPA to start regulating the “pollutant” CO₂. They ought to figure a way to tie mortgages to global warming. Then things will really get better.

Yes, a disconnected rant today. All I know is that I have been prudent and actually have saved to buy a house, did not try to purchase anything I couldn’t afford, and now I will be asked to pay for the mistakes of all the experts and fools who brought this on.

In any government bailout, the first thing I would require is that any executive of the firms that are being helped would lose all of their personal assets. Every penny. Then I’d sue the traders and stockholders to recover more. I’d do all that before I started taking money from innocent civilians.

As it is, the executives from Fannie Mae, Lehman Brothers, etc., will all walk away very rich men. They will be rewarded.

And the government will continue to bloat.

In today’s New York Post (p. 31) runs a full page add by CNN. The ad itself looks like a PowerPoint presentation, that is, a dull layout driven by bullet points. Here are the first three:

1. #1 Most Watched Cable News Network Across Both DNC and RNC Conventions for P25-54, P18-49, & P18-34
2. #1 Most Watched Cable News Network at 10PM Across Both Conventions
3. #1 News and Information Site During Both Conventions-CNN.com

In the first bullet, what is that odd “P25-54″? What in the world could that be? The ad, even in the fine print, nowhere says. But we can guess is means “people aged 25 to 54″. OK, so people aged 25 to 54—a good slice of people—according to “sources”, liked CNN. Sounds impressive, but there are two misleading elements.

The first is that it was the most watched “cable news network”, which means that the “non-cable news networks” might have been watched by more people. We can guess that this is true else the ad would have touted that CNN was the most watched network period.

The second problem is the bizarre way they sliced the age groups. The 18-49 groups entirely contains the 18-34 group, does it not? So why mention the smaller-sized group? Does it mean that the 35-49 years olds did not prefer CNN? But the 35-39 year olds are certainly among the 25-54 years olds, which was the first group mentioned.

There is no making sense of any of this except by supposing that CNN scrounged through the data to find any hint of subgroups that supported their “#1″ contention. Experience shows that you can do this for any statistical analysis, which is why so many rightly suspect whatever statisticians have to say.

The second bullet point is just as screwy. How many different networks did they compare anyway? The barely readable small print says “CNN, FNC, and MSNBC.” So they only had two competitors, the last of which, MSNBC, has always struggled for viewership. Coming in number 1 in a few categories with only one real competitors is not a laudable achievement.

But it also means that CNN must have lost, probably to Fox, in the 7p-8p slot, the 8p-9p slot, the 9p-10p slot, and the 11p-12a slot, where are 4 out of the 5 slots the small print says the “sources” checked. Losing 80% of the time hardly makes you number 1.

The third bullet is more tepid. The “source” says “Information Site” means “Current events and Global News Category.” We have no idea how many other sites were compared against CNN, nor how many other categories—say Analysis, Opinion, Politics, and so on—were checked.

Still, for cheating, the third bullet is best, because it’s a rare person will be pause much over the claim, nor will most browse the small print.

In any case, CNN should just have presented their fourth bullet, which was

• #1 Most Trusted & Credible Name in News

This bullet is so vague it can mean anything. It’s crafted so that readers can take any meaning they like from it. Most people will be left with a dull sense of the importance of CNN.

This ad, while fairly misleading, only earns a 4 (out of 10) on the Briggs Statistical Deception Scale.

For the teachers out there, these ads often make good homework problems for students. Chopping up an ad into component parts and reading it critically is always great fun for the students. Especially when you find deceitful ads.