William M. Briggs, Statistician

UPDATE: Christian Toto, over at Pajama’s Media, has seen the HBO Generation Kill and says “The new HBO miniseries on Iraq is well-executed, but its anti-war bias is clear.” Make sure to also read the comments.

This tip in from Kyle Smith, from today’s New York Post. Since the subject came up yesterday about fictional accounts of military action, we have here, at LiveLeak.com, hundreds of actual scenes filmed by the soldiers themselves. Smith’s story is called Wartube.

Some examples. One:

Two:

I had no idea of this site before today. But I would imagine that whatever Hollywood offers, no matter how “gritty and realistic”, cannot compare to the actual real reality as delivered directly by the soldiers. Of course, the soldiers’ own story suffers only one flaw when compared to fictionalized accounts: no slow motion (joking, just joking).

Finding the Target: The Transformation of American Military Policy by Frederick W. Kagan, 2006. Encounter Books. Recommendation: read

This book is an excellent accounting of the theories that have gripped and influenced American military thinking and planning since the Vietnam war.

Theories?

If all your information on the military has come from Hollywood (and there is a new series about Iraq out on HBO, surely this time written by writers who actually served and are thus knowledgeable), then it might come as a surprise that when planning a war you actually have to decide what to hit, what resources are needed, when those resources should be in place, what will happen in theater and out of it, the political consequences, and on and on. These decisions are made with reference to a guiding doctrine, a.k.a. a theory.

Since Clausewitz, a leading theory has been to attack an enemy’s “centers of gravity”. Destroy those, the theory goes, and the enemy collapses in confusion. Maybe so. But what is a center of gravity? Does this mean you try to kill as many troops in the field as possible? Or instead commit your resources to disrupting enemy supply lines, or perhaps the lines of communication and control? Or do you, as happened at the very beginning of Iraqi Freedom, attempt to take out the leadership (an effort, you will recall, which failed)? All good questions, the answers to which should depend on the situation. The danger is that people can pay more attention to the guiding theory—to what the theory says reality should be like—than to actual reality itself. This common human failing is found in war just as it is in other areas.

There is also the danger of rushing in, say after an unexpected attack of your country, and not having any plan:

[T]hey find it difficult—albeit no less important—to identify clear, achievable strategic aims. There is an emotional temptation to want to ‘do something’ without first clearly understanding what political purpose that ’something’ is supposed to accomplish.

Kagan repeatedly emphasizes that military actions are subservient to, or an extension of, a country’s political aims. Just killing the enemy is not enough. The way that enemy is killed or defeated must be done in such a way to further the political aims. The lack of these thoughts harmed the Iraqi war. As is well known by now, the hostilities themselves were over very quickly. The war plan was to “topple the regime” as fast as possible. This was “mission accomplished.” But in toppling the regime, nothing took its place, and chaos prevailed. The problem was the enemy was not captured, they was instead allowed to disperse, taking their weapons with them, the result of which was the insurgence.

The situation in Iraq was not turned around until more boots were on the ground, handling things in the old fashioned way, opposite to dictates of the “revolution in military affairs” and “transformative” theories then touted by the leadership.

Kagan also takes to task the latest theories that holds some in thrall: Network Centric Warfare, or NCW. This is the idea that the miracles of the “Information age” will “revolutionize” and “transform” forever our view of the “battlespace” (the old term “battlefield” deemed musty). Generals, using these things called computers, will soon be able to see what every platoon-leading lieutenant sees, and so will be able to direct the battlespace more effectively. Information overload? Don’t bother me with details. Kagan sums up his objections to NCW:

First, it is a solution in search of a problem. Second, the technical requirements needed to produce the capabilities sought and promised are unattainable in the real world. Third, it proceeds from a misunderstanding of the nature of war…The NCW visionaries imagine a world in which the eternal race between offense and defense ands in our favor—we will be able to see everything and the enemy will be about to nothing about it. This notion is preposterous.

Instead, Kagan advocates the obvious strategy: plan for the situations you are most likely to face. You might still be wrong, but you, by definition, have the best chance of being right. Do not ask for “revolutionary” technologies, but build better weapons from known technologies.

Other topics are discussed. For example: “The Army still maintain garrisons as though it were preparing to subdue the Sioux and Apache once again.” These historical dispositions “impose significant delays” on deployment and offer the enemy “numerous bottlenecks to strike.” But to try and change base and post locations is a mighty political task. Try suggesting to your congressperson—Democrat or Republican—that the base in their state is aptly located and see what happens. Politicians, as ever, will usually opt for what is best for themselves and not the country.

The book is an intelligent, readable overview of military policy planning and I highly recommend it.

This story has been making the rounds (I first heard of it from Roger Kimball’s blog). It’s so incredibly asinine that it deserves broad exposure.

The headline from England’s Telegraph is Toddlers who dislike spicy food ‘racist’. The article leads:

Toddlers who turn their noses up at spicy food from overseas could be branded racists by a Government-sponsored agency.

The National Children’s Bureau, which receives ?12 million a year, mainly from Government funded organisations, has issued guidance to play leaders and nursery teachers advising them to be alert for racist incidents among youngsters in their care.

This could include a child of as young as three who says “yuk” in response to being served unfamiliar foreign food.

The guide is 366 pages long! Yuk!

Nurseries are encouraged to report as many incidents as possible to their local council. The guide added: “Some people think that if a large number of racist incidents are reported, this will reflect badly on the institution. In fact, the opposite is the case.”

That is to say, nursery workers are encouraged to rat out small children to the local Party Leaders. No doubt horrific injustices like denying a love of curry will be noted on the tots’ permanent records. Can re-education day camps be far behind?

This reminds of a guy (whose name I expurgated from my memory) invited to campus when I was still at professor at Central Michigan. The topic was—what else?—diversity. This guy, who had many letters after his name, was touting a theory called micro-racism. These are racists acts that are so small that the person perpetrating them, and the person being disparaged, cannot see them. Only people specially trained could spot and analyze the atrocities.

Professors were told that when overhearing something shocking like—if you have a weak stomach, please do not read further—”Where are you from?”, we should recognize the ill intent behind the words and caution the student to modify his behavior.

That’s the only example of “micro-racism” that I can recall. Not too many examples were given. This of course makes it easy for the PC Police to label anything they want as “micro-racism.” Only an exceptionally dull person could not take any phrase whatsoever and twist it into an example of intolerance.

I don’t have the National Children’s Bureau’s guide, but I can only hope they include material on micro-racism.

I like Jessica Rabbit. Her voice, I mean—Kathleen Turner. Throaty, a hint of edgy raspiness, alluring, damn sexy.

But I just found out that I was wrong. Turns out I do not like sultry voices like I think I do. Instead, peer-reviewed research has proved that “High-pitched voices are most attractive.” This can only mean that I require re-education to correct my incorrect choice. Experts have weighed in!

Actually, of course, and because I can’t continue being facetious, I want to highlight a very common piece of poor “science” journalism, based on questionable research. I want to dissect this article paragraph by paragraph (don’t worry, it’s not long), to show how to spot garbage.

The article, entitled “High-pitched voices are most attractive“, by Dave Munger over at Cognitive Daily, summarizes a paper by Feinberg and others entitled “The role of femininity and averageness of voice pitch in aesthetic judgments of women’s voices” in the journal Perception.

First, the title (the reporter’s, not the paper’s). It is false. Not just “maybe not true” but false as in “ridiculously untrue.” It is not true in my case, nor in many, many other cases. So why would somebody write such a headline? Laziness, probably.

The article starts with some unmemorable fluff about some celebrity, finally moving to the sentence “In general we perceive higher voices as more feminine.” This is true. But it is one of those statements that every single human already knew was true—who didn’t know females had higher voices?—only it could be “proved” true until “research” said it was. This unfortunate attitude is now commonplace. It isn’t true until some “researcher” does “research” to show it’s true. Nonsense.

Let’s not lose sight of what they author is trying to prove: “High-pitched voices are most attractive.” What is the evidence for this (false) statement? Well, this: researchers “recorded the voices of 123 young women as they pronounced five vowel sounds: ah, ee, eh, oh, and oo. Then ten male volunteers rated each voice for attractiveness.”

How many men? 10, or “ten”, or “just one more than nine.” No doubt these men were chosen from a broad population to ensure to capture a wide range of opinion. Just kidding. They weren’t. Like many papers, the “researchers” grabbed a bunch of men who were close at hand and hoped for the best. That is, these young American men were assumed to have the same tastes as Vietnamese, Yemenese, Chinese, Siamese, and other-eses, of all age, economic, social, etc. backgrounds. The same comments can be made about the women. Did they all speak English? Have the same accent? Did they vary their tone to fit the circumstance? Etc. etc.

The chance that the sample used was representative of all humans? The words “near zero” come to mind. And we haven’t even begun to ask why just five English vowel sounds would be representative of all sounds, nor how the manner of speech and the words used are mixed up in how attractiveness is rated.

A statistical graph is then shown, which I do not have the heart to reproduce. It is a scatterplot, showing the relationship of the frequency of the spoken vowel sounds with the attractiveness rating. A straight (and curved) line is drawn through the points. It is said to be “statistically significant.” This means that the p-value of the slope of the regression line is less than 0.05. What is a p-value? It says that the chance of seeing a statistic (which is a function of the estimate of that slope) larger than the one we actually got given the experiment were an infinite number of additional times and given the slope is actually 0. Yes, complicated and confusing. That’s classical statistics for you.

But, statistically speaking, the line is crap. Pick a frequency, a low one, like 180-190 Hertz. Attractiveness ratings range from just over 2 to about 5.5, just the same as they do for higher frequencies. For one high frequency lady (about 250 Hertz), the attractiveness was low; and there were far fewer high pitched sounds to sample from. The researchers have made the common error of conflating the “statistical significance” of the parameter (slope) of the regression line with the actually difference in observable attractiveness ratings. We do not see which women was which — some women’s voice might have been better than others regardless of pitch. To prove that the reporter does not understand what he has just seen, he repeats “Higher-pitched voices are more attractive” right after discussing the graph.

There are other problems with this graph. There are about 123 numbers. Fine. There were 123 ladies. But each recorded 5 sounds, and there were 10 men. Shouldn’t there be 123 x 5 x 10 = 6150 points? Because there are not, it means some prior data manipulation has taken place. A summarization has been done (the mean of attractiveness per women). I have no idea how this summarization would effect the results. Nor would the researchers, because it would require some sort of model, which is not present.

The researchers knew at least some of their limitations because, as the reporter reports, they asked “[W]ill simply raising the pitch of a female voice make it more attractive, or are there other factors involved?” So they picked “three groups of five voices…:five low-pitched, five medium-pitched, and five high-pitched.” After this— I swear this is true—”a computer program was used to artificially raise and lower the pitch of each of these voices.” Then “volunteers listened to high- and low-pitch versions of each voice and indicated which was more attractive.”

Again, they claim that higher pitched voices were picked slightly more often as being attractive (another bad statistical graph is shown).

But wait a minute. A “a computer program was used to artificially raise and lower the pitch of each of these voices”? Yes, “”a computer program was used to artificially raise and lower the pitch of each of these voices.”

Good thing that was the end of the article, because I couldn’t take any more. From 123 women, just 15 (that’s “three groups of five voices”) were supposed to represent all human females everywhere. These 15 voices were changed by a computer algorithm to sound how a computer programmer thought they should sound. After they made the voices sound like they wanted them to sound, they asked other people how they sounded. Oh, good grief.

If the overall finding is that “men prefer high-pitched voices”, then I would say that it is true, but everybody already knew it was true because everybody knows women have higher-pitched voices than men, and that men prefer women (generally), therefore they must prefer higher-pitched voices (generally).

But just writing that into a paper will not get you published.

Here’s the problem. You are a scientist, working on measuring the levels of aragonite in ocean water. It’s not very sexy and nobody beyond a small cadre seems to care. But it’s grant time and you and your team are “figuring out how to make the issue more potent” so that you can bring in the bucks.

How do you do it?

The first thing you should immediately consider these days is “turning up the heat on the issue through the media.” However, convening a press conference on “The Importance of Aragonite in Ocean Water” is unlikely to interest even the New York Times.

You need to be clever. Your job in “expanding awareness” has to start with a snappier moniker. You need a term that is “easy to comprehend” and, if you’re lucky, sounds “alarming.”

Renaming is thus “a critical step.”

So you ponder. Then you recall that aragonite levels are related to the amount of diffused carbon dioxide in ocean water. Some chemistry helps: when CO₂ dissolves in water it lowers that water’s pH. And what is lowering pH sometimes called? Acidification!

Success! Not only is this a fantastically frightening term, it drives “home the idea that carbon dioxide [i]s a pollutant.”

Your next step is to find a PR firm whose specialty is to “link researchers with policy-makers and the media.” The good news is that there are no shortage of such places.

Of course, you have to be honest about “the” science and the uncertainties (as you understand them). But if you’re lucky, even the possibility, no matter how small, of risk will be enough to frighten Congress into action.

I think we can agree “the acidification story provides a model of how to get science on the congressional agenda.”

A fuller account of this fascinating and inspirational story may be found here (Nature magazine, again leading the way).

Nature magazine reports this headline: Condemned to single-sex life by climate change.

They are talking about a species of lizards called tuatara that live “on about 30 small islands in New Zealand?s north.” The disgusting, scaly creatures are in exile on those islands because they have everywhere else been “wiped out by predators.” No word on who or what these predators are or why the predators cannot follow the tuatara to the islands and thus continue their campaign of herpetological genocide.

Anyway, the lizards are about to go extinct and it’s all your fault. It seems that when the weather is hot, more male tuatara lizards are born than female lizards. And we all know what happens when there are more boy than girl lizards. It becomes impossible to get a date and procreate.

This “doomsday prediction”, we are told by researchers, is assured because of (what else?) global warming.

How do the researchers know this? Why, a computer told them so.

Previous computers did not tell them so, which forced the researchers to reprogram them, this time incorporating in their models “physics of heat transfer with terrain data.” Well, that is impressive. The researchers then “simulated climate change and then monitored its effect on specific points across the island.”

What they found was shocking: Rampant maleness, which naturally carries with it the consequence of enforced bachelorhood.

For those of you who are not as computer savvy as I, let me summarize. Researchers programmed a computer to show that when the temperature rises, fewer female lizards are born. They then told the computer that temperatures were in fact rising. The computer then said “fewer female lizards are born.”

The researchers pored over this result and came to the conclusion that “warmer temperatures caused by global warming imply fewer female lizards will be born.” They wrote this in a paper which was duly summarized at Nature. Science in action!

All might not be lost because, the researchers suggest, the lizards might be “translocated” ( = moved) to cooler climes. I just hope that those mysterious predators aren’t in the new translocations.

Another piece of data is in that shows money does not correlate with intelligence.

“Artist” Martin Creed (pictured above) created a “work” called 850, which he will exhibit at the well-known Tate Britain art gallery starting today.

The “work” consists of having joggers, once every thirty seconds, trot through the museum.

Yes, you read that right. Joggers, wearing shorts and looking like they came from the park, will run lightly through a hall or two in the name of “art.”

Guardian writer Adrian Searle claims that the wonderful thing about this “art” is “that it is gloriously pointless.” It’s not surprising the paper should feel that way, since much of its reporting falls into this category. Searle argues that people should not try to decide whether 850 is “art” but “whether the work captures the imagination, whether it gives pleasure and makes people think.”

So, on this theory, I could put a certain piece of Mr Searle’s anatomy in a vice and start to twist, an act which is certainly imaginative and would give me some pleasure. It would also cause Searle to do some serious thinking. But would he call it art?

People should not feel anger or despair over the sort of idiocy like 850, now common in the “art” world. They should instead view it as a chance to raise their income bracket. Since rich people—those people that run galleries and buy and sell “art”—are now utterly incapable of judging quality, and are dead scared of admitting their ignorance, the door is wide open for any “artist” to sell them anything. The only key seems to be that the “work” has to be completely asinine, childish, devoid of any value, and, of course, politically correct.

It also cannot be cheap. The more exhorbitantly priced your excrescense, the better chance it has to sell. For you must understand that the sole purpose of this “art” is to allow its owner to boast that he owns it. Or, in the case of the Tate, to claim that it is unique.

Chris Anderson, over at Wired magazine, has written an article called The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.

Anderson, whose thesis is that we no longer need to think because computers filled with petabytes of data will do that for us, doesn’t appear to be arguing serious—he’s merely jerking people’s chains to see if he can get a rise out of them. It worked in my case.

Most of the paper was written, I am supposing, with the assistance of Google’s PR department. For example:

Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required.

He also quotes Peter Norvig, Google’s research director, who said, “All models are wrong, and increasingly you can succeed without them.”

Lastly,

The scientific method is built around testable hypotheses….The models are then tested, and experiments confirm or falsify theoretical models of how the world works…But faced with massive data, this approach to science ? hypothesize, model, test ? is becoming obsolete.

Part of what is wrong with this argument is a simple misconception of what the word “model” means. Google’s use of page links as indicators of popularity is a model. Somebody thought of it, tested it, found it made reasonable predictions (as judged by us visitors who repeatedly return to Google because we find its link suggestions useful), and thus became ensconced as the backbone of its rating model. It did not spring into existence simply by collecting a massive amount of data. A human still had to interact with that data and make sense of it.

Norvig’s statement, which is false, is typical of the sort of hyperbole commonly found among computer scientists. Whatever they are currently working on is just what is needed to save the world. For example, probability theory was relabeled “fuzzy logic” when computer scientists discovered that some things are more certain than others, and nonlinear regression were re-cast as mysterious “neural networks,” which aren’t merely “fit” with data, as happens in statistical models, instead they learn (cue the spooky music).

I will admit, though, that their marketing department is the best among the sciences. “Fuzzy logic” is absolutely a cool sounding name which beats the hell out of anything other fields have come up with. But maybe they do too well because computer scientists often fall into the trap of believing their own press. They seem to believe, along with most civilians, that because a prediction is made by a computer it is somehow better than if some guy made it. They are always forgetting that some guy had to first tell the computer what to say.

Telling the computer what to say, my dear readers, is called—drum roll—modeling. In other words, you cannot mix together data to find unknown relationships without creating some sort of scheme or algorithm, which are just fancy names for models.

Very well—there will always be models and some will be useful. But blind reliance on “sophisticated and powerful” algorithms is certain to lead to trouble. This is because these models are based upon classical statistical methods, like correlation (not always linear), where it is easy to show that it becomes certain to find spurious relationships in data as the size of that data grows. It is also true that the number of these false-signals grow at a fast clip. In other words, the more data you have, the easier it becomes to fool yourself.

Modern statistical methods, no matter how clever the algorithm, will not being salvation either. The simple fact is that increasing the size of the data increases the chance of making a mistake. No matter what, then, a human will always have to judge the result, not only in and of itself, but how it fits in with what is known in other areas.

Incidentally, Anderson begins his article with the hackneyed, and false, paraphrase from George Box “All models are wrong, but some are useful.” It is easy to see that this statement is false. If I give you only this evidence: I will throw a die which has six sides, and just one side labeled ‘6′, the probability I see a ‘6′ is 1/6. That probability is a model of the outcome. Further, it is the correct model.