Jill Filipovic wrote an opinion column for The Guardian yesterday, arguing against the practice of women taking their husbands' names when they get married. It ended up linked on Jezebel and found its way to my Facebook feed where one particular statistic caught my eye. Filipovic claimed that 50% of Americans think a women should be legally required to take her husband's name.

First, some quick clarification of my biases here. Although I write under a hyphenate, I never have legally changed my name. I've never had a desire to do so. In my private life, I'm just Maggie Koerth and always will be. That said, I personally take issue with the implication at the center of Filipovic's article — that women shouldn't change their names and that to do so makes you a bad feminist. For me, this is one of those personal decisions where I'm like, whatever. Make your own choice. Just because I don't get it doesn't mean you're wrong.

But just like I take objection to being all judgey about personal choices, I also take objection to legally mandating personal choices, and I was kind of blown away by the idea that 50% of my fellow Americans think my last name should be illegal.

So I looked into that statistic. And then I got really annoyed.

First off, Filipovic doesn't cite a source for that stat. Some of her other numbers — specifically, that 10% of Americans think that keeping your name means you aren't dedicated to your marriage — are cited, with a link to an Atlantic Wire article that links to a Livescience piece about a survey of a couple hundred students at a small Midwestern college. That study, itself, wasn't actually meant to tell you what the American public thinks as a whole. It was meant to compare changing attitudes between 1990 and 2006 in a place that was specifically chosen because it was likely to be fairly conservative. It was specifically meant to contrast with previous research that had overly focused on the choices and attitudes of upper-income East Coasters. In other words, the data doesn't say what Filipovic says it does.

The 50% statistic comes from a 2011 paper, published in the journal Gender & Society. The whole PDF is online, if you want to read it.

In that survey, 22.3% of respondents strongly agreed with the question, "In the past, some states legally required a woman to change her name to her husband’s name. Do you strongly agree, somewhat agree, somewhat disagree, or strongly disagree that this was a good idea?" Another 27.6% somewhat agreed. And that adds up to 49.9%.

But it doesn't tell the full story.

First off, this was a survey of a little more than 800 people, almost half of whom were from Indiana. They were randomly chosen — so that's better than, say, a survey of college students — but it's still a far cry from saying, "This is what half of all Americans believe."

Second, there's a difference between "strongly agree" and "somewhat agree". Just like there's a difference between "somewhat disagree" and "strongly disagree". If you've ever taken a survey where those were your only choices, you know that it's often difficult to fit your actual beliefs into the boxes. Although the authors did ask follow-up questions, the paper doesn't discuss them in this particular context, so it's hard to say exactly what the people answering "somewhat agree" actually meant to say. There is some evidence in the paper, though, that what was really being expressed here was a belief in the rightness of families sharing an identity. On another question, "It's okay for a man to take his wife's name when he marries," a full 53.5% either agreed or strongly agreed. (Although some of those people seemed to agree with the idea in a way that suggested they found it unlikely to actually happen.) And the authors of the paper even ended up connecting both these responses to strong "collectivist" or "individualist" ideas about marriage and family.

Finally, while there were certainly people surveyed who thought women should change their names because of religious ideology or what many of us would probably consider outdated notions of who in the relationship "belongs" to whom, what respondents thought about name changes didn't necessarily reflect what they thought about female equality. Sixty-seven percent of these people disagreed with the idea of strict "man as breadwinner, woman in the home" gender roles. Eighty-two percent thought that working mothers could have just as good of a relationship with their children as stay-at-home moms. And 80% disagreed with the idea that it was more important for a woman to support her husband's career and goals than her own.

Oh, and it's also worth noting that the answers on name-change questions split much more obviously along cultural lines — race, education level, income, where you live in the country — than did the answers to the questions on gender roles, which were much more uniform. Essentially, there's some evidence here that what you think about name changes has more to do with the cultural expectations you live with than it does with what you actually think about women.

All of that kind of serves to undermine, rather than support, Filipovic's position. The survey doesn't tell us what all Americans believe. But it does tell us that it's perfectly possible to feel uncomfortable with the idea of a woman not changing her name upon marriage and still feel pretty comfortable with the idea that women are people. As a feminist, it's that latter idea I actually care about.

So why does this bother me so much?

Here's the thing. I grew up in a fairly conservative and religious culture, listening to Christian radio and hearing all sorts of "outrageous" news about how liberals were oppressing people and trying to take away our ability to choose our own way of life.

As a teenager and young adult, I started looking into those claims more closely and found that the vast majority weren't true. These situations and statistics weren't ever just made up out of whole cloth, but they were deeply misrepresented and contorted in order to support a pre-determined thesis. The closer you looked at what actually happened, what had actually been said, how people surveyed had actually responded, the more the intended sense of outrage and oppression vanished in a puff of logic.

That experience made me a skeptic. It also made me feel pretty damn betrayed and used.

Today, I'd classify myself as fairly liberal. But it still makes me angry when people misuse, misconstrue, and misrepresent information in order to manipulate me into feeling oppressed and outraged. It still pisses me off when all I have to do is spend 15 minutes reading in order to easily figure out that "those people" are not actually out to get me. And I don't really care whether it's "my side" or "their side" doing it. Either way, it makes me angry.

Half the people I meet in my daily life do not want to take away my right to choose my own last name. (Or, at least, there's no evidence of that here.) Whether or not you change your last name — and whether or not you think married women should change their last name — is not the strongest predictor of what you think about women's equality. (At least, that doesn't seem to be the case according to this survey.)

If Jill Filipovic thinks women should keep their own last names, well, great. I enjoy keeping mine. But she should be able to make that point without trying to scare people and without trying to misrepresent what a name change does and doesn't mean about our personal beliefs.

Image: marriage license, a Creative Commons Attribution (2.0) image from sadsnaps's photostream

They got the OU's Dr Ian Johnston to stress-test a 2X2 Lego in a hydraulic testing machine, increasing the pressure to some 4,000 Newtons, at which point the brick basically melted. Based on this, they calculated the maximum weight a 2X2 brick could bear, and thus the maximum height of a Lego tower:

The average maximum force the bricks can stand is 4,240N. That's equivalent to a mass of 432kg (950lbs). If you divide that by the mass of a single brick, which is 1.152g, then you get the grand total of bricks a single piece of Lego could support: 375,000.

So, 375,000 bricks towering 3.5km (2.17 miles) high is what it would take to break a Lego brick.

"That's taller than the highest mountain in Spain. It's significantly higher than Mount Olympus [tallest mountain in Greece], and it's the typical height at which people ski in the Alps," Ian Johnston says.

"So if the Greek gods wanted to build a new temple on Mount Olympus, and Mount Olympus wasn't available, they could just - but no more - do it with Lego bricks. As long as they don't jump up and down too much."

How tall can a Lego tower get?

More or Less: Opinion polling, Kevin Pietersen, and stacking Lego 30 Nov 2012 [MP3] ]]> http://boingboing.net/2012/12/04/tallest-possible-lego-tower-he.html/feed 71 http://boingboing.net/2012/11/30/everything-you-eat-is-associat.html http://boingboing.net/2012/11/30/everything-you-eat-is-associat.html#comments Fri, 30 Nov 2012 20:55:15 +0000 Xeni Jardin http://boingboing.net/?p=197588 Shutterstock. Fried chicken gave the model in this stock photo cancer of the double chin. Sarah Kliff at the Washington Post digs into new research out today from The American Journal of Clinical Nutrition.]]>

Image: Shutterstock. Fried chicken gave the model in this stock photo cancer of the double chin.

Sarah Kliff at the Washington Post digs into new research out today from The American Journal of Clinical Nutrition. She writes about correlation and causality, and how to read statistics more intelligently.

Snip:

“I was constantly amazed at how often claims about associations of specific foods with cancer were made, so I wanted to examine systematically the phenomenon,” e-mails study author John Ioannidis ”I suspected that much of this literature must be wrong. What we see is that almost everything is claimed to be associated with cancer, and a large portion of these claims seem to be wrong indeed.”

Among the ingredients in question for their purported relation to cancer risk: veal, salt, pepper spice, flour, egg, bread, pork, butter, tomato, lemon, duck, onion, celery, carrot, parsley, mace, sherry, olive, mushroom, tripe, milk, cheese, coffee, bacon, sugar, lobster, potato, beef, lamb, mustard, nuts, wine, peas, corn, cinnamon, cayenne, orange, tea, rum, and raisin.

Now: combine all of them into one recipe and do the study again, I say.

]]> http://boingboing.net/2012/11/30/everything-you-eat-is-associat.html/feed 43 http://boingboing.net/2012/11/12/nate-silvers-the-signal.html http://boingboing.net/2012/11/12/nate-silvers-the-signal.html#comments Mon, 12 Nov 2012 20:52:26 +0000 Rob Beschizza http://boingboing.net/?p=193680 Nate Silver's been in the news a lot these last few days: looking at some stories, you'd think he'd won the election, not Mr. Obama. A statistician, his rigorous polling analysis riled, then humiliated political pundits, whose imaginary political horse-race was rejected by Silver's cold, hard numbers.

And what numbers they were. His "prediction"--though really just the most likely probability among many scenarios offered by his model--nailed the electoral college total on the night.

I've just read his book, The Signal and The Noise, and while it isn't as approachable as you might expect, that's what makes it interesting. Silver's insistence is that the quintillions of bytes of data at our disposal actually make prediction harder, not easier, and that we should not place too much stock in forecasts. Nor is it enough for us to to indulge brilliant but unanchored insights of the type offered by Malcolm Gladwell and Freakanomics. Planning for the relative probabilities of many possible outcomes is more useful than going all-in on specific predictions.

He takes us through our failures and successes. We're still lost at sea when it comes to disaster prediction, but (as he demonstrated last week) quite good at forecasting election results. Silva homes in on the work of mathematician Thomas Bayes as the right approach to understanding how statistics are most rationally interpreted to human ends.

By the end, though, it's surprising how unfulfilling it all seems. The Insight Industry's easy answers sure do go down easier than a dissertation on Big Data's ineluctable disinterest in them. Here, we are instead shown how prediction-makers screw up by treating their work as esoteric, by hiding uncertainties and inexactitudes to appear precise and decisive.

Which brings us back to the pundits, red-faced but curiously immobile in the stew of their failures. I don't quite buy that accepting these complexities will fill the chasm between their inanity and numerate reason. Though a place that seems rich with human promise, what emerges from the dark is a kind of interpretive accounting: much less interesting than whatever probabilities may finally be offered, and harder to sell than pompous old men just telling their peers how it is. It's such a difficult story to tell: even if you accept it, it's always less fun than the one where Joe Scarborough was made a total fool of by a brilliant young statistician.

I'm not helping, I know. But the pundits aren't going anywhere.

The Signal and the Noise: Why So Many Predictions Fail-but Some Don't [Amazon]]]> http://boingboing.net/2012/11/12/nate-silvers-the-signal.html/feed 20 http://boingboing.net/2012/11/07/particle-physicists-not-yet-wi.html http://boingboing.net/2012/11/07/particle-physicists-not-yet-wi.html#comments Wed, 07 Nov 2012 15:15:52 +0000 Maggie Koerth-Baker http://boingboing.net/?p=192645 that is several standard deviations away from the 99.99995% confidence that the particle physicists would need to declare the election won.]]> that is several standard deviations away from the 99.99995% confidence that the particle physicists would need to declare the election won. (This is satire, obviously.) Via Jennifer Ouellette.]]> http://boingboing.net/2012/11/07/particle-physicists-not-yet-wi.html/feed 9 http://boingboing.net/2012/11/05/surviving-a-plane-crash-is-sur.html http://boingboing.net/2012/11/05/surviving-a-plane-crash-is-sur.html#comments Mon, 05 Nov 2012 14:51:26 +0000 Maggie Koerth-Baker http://boingboing.net/?p=192187

I'm a nervous flyer. But I'm a lot better at it then I used to be. That's because, a few years ago, I learned that it's actually pretty common to survive a plane crash. Like most people, I'd assumed that the safety in flying came from how seldom accidents happened. Once you were in a crash situation, though, I figured you were probably screwed. But that's not the case.

Looking at all the commercial airline accidents between 1983 and 2000, the National Transportation Safety Board found that 95.7% of the people involved survived. Even when they narrowed down to look at only the worst accidents, the overall survival rate was 76.6%. Yes, some plane crashes kill everyone on board. But those aren't the norm. So you're even safer than you think. Not only are crashes incredibly rare, you're more likely to survive a crash than not. In fact, out of 568 accidents during those 17 years, only 71 resulted in any fatalities at all.

I was talking about this fact with a pilot friend over the weekend, and he mentioned one crash in particular that is an excellent example of the statistics in action. On July 19, 1989, United Airlines Flight 232 lost all its hydraulic controls and landed in Sioux City, Iowa, going more than 100 mph faster than it should have been. You can see the plane breaking apart and bursting into flames in the video above. Turns out, that's what a 62% survival rate looks like. (All the pilots you can hear talking in the video survived, too.)

Read more about United Airlines 232 on Wikipedia

Read the full NTSB report from 2001

In 2007, Popular Mechanics examined 36 years of NTSB reports and found that the majority of surviving passengers were sitting in the back of the plane. But that seems to depend a lot on the specifics of the crash and may not be a reliable predictor of future results.

In particular, you have to remember that, while Nate Silver gives President Obama a 77.4 percent chance of winning the presidential election, that's not the same thing as saying that Obama is going to win.

Suppose a weather forecasting model predicts that the chance of rain in Chicago tomorrow is 75 percent. How do we determine if the model produces accurate assessments of probabilities? After all, the weather in Chicago tomorrow, just like next week’s presidential election, is a “one-off event,” and after the event the probability that it rained will be either 100 percent or 0 percent. (Indeed, all events that feature any degree of uncertainty are one-off events – or to put it another way, if an event has no unique characteristics it also features no uncertainties).

The answer is, the model’s accuracy can be assessed retrospectively over a statistically significant range of cases, by noting how accurate its probabilistic estimates are. If, for example, this particular weather forecasting model predicted a 75 percent chance of rain on 100 separate days over the previous decade, and it rained on 75 of those days, then we can estimate the model’s accuracy in this regard as 100 percent. This does not mean the model was “wrong” on those days when it didn’t rain, any more than it will mean Silver’s model is “wrong” if Romney were to win next week.

What Silver is predicting, in effect, is that as of today an election between a candidate with Obama’s level of support in the polls and one with Mitt Romney’s level of support in those polls would result in a victory for the former candidate in slightly more than three out of every four such elections.

Read the full story at Salon.com

It is worth ending this cautionary tale with a review of the BLS's own occupational handbook projection for musician/singer employment in the near future. Note that the handbook cites a much higher employment figure for both trades in 2010 than mentioned in the above tables: about 176,200 musicians and singers. That's because it comes from the Bureau's National Employment Matrix, I was told, which adds additional data sources.

Employment for musicians and singers is expected to grow by ten percent over the decade—"about as fast as the average for all occupations," the government notes:

The number of people attending musical performances, such as orchestra, opera, and rock concerts, is expected to increase from 2010 to 2020. As a result, more musicians and singers will be needed to play at these performances.

There will be additional demand for musicians to serve as session musicians and backup artists for recordings and to go on tour. Singers will be needed to sing backup and to make recordings for commercials, films, and television.

Have we lost 41 percent of our musicians? Depends on how you (the RIAA) count ]]> http://boingboing.net/2012/10/07/fact-checking-the-riaas-clai.html/feed 14 http://boingboing.net/2012/10/02/what-a-dead-fish-can-teach-you.html http://boingboing.net/2012/10/02/what-a-dead-fish-can-teach-you.html#comments Tue, 02 Oct 2012 19:51:57 +0000 Maggie Koerth-Baker http://boingboing.net/?p=184176 The methodology is straightforward. You take your subject and slide them into an fMRI machine, a humongous sleek, white ring, like a donut designed by Apple.]]>

The methodology is straightforward. You take your subject and slide them into an fMRI machine, a humongous sleek, white ring, like a donut designed by Apple. Then you show the subject images of people engaging in social activities — shopping, talking, eating dinner. You flash 48 different photos in front of your subject's eyes, and ask them to figure out what emotions the people in the photos were probably feeling. All in all, it's a pretty basic neuroscience/psychology experiment. With one catch. The "subject" is a mature Atlantic salmon.

And it is dead.

Functional magnetic resonance imaging (fMRI) is a powerful tool that allows us to capture incredible amounts of information about what happens in our brains. It's relatively new — neuroscientists began using fMRI in the early 1990s — and it produces colorful images that help bring numbers to life for the general public.

All of those things are strengths for fMRI. Unfortunately, they're also all weaknesses. New tools vastly expand our understanding of the human body ... but they also mean that we have to develop new standards so that different studies using the same tool can actually be compared to one another. Images of the human brain help make science more understandable ... but they can also be incredibly misleading when the public doesn't have a good idea of what the pictures show. Amassing vast quantities of information is great ... but it also makes it easy to end up with false positives — coincidences of chance that look like something a lot more important.

Enter the dead salmon.

In 2009, a team led by neuroscientist Craig Bennett and psychologist Abigail Baird ran an fMRI experiment using the salmon as their subject. Not only did they really put a dead (and frozen) fish into an fMRI machine, later analysis of their data actually produced evidence of brain activity — as if the dead fish were thinking. It wasn't, of course. But Bennett's and Baird's research — which recently won a 2012 IgNobel Award — was meant to show how easily scientists can mislead themselves and why well-done statistics are vital.

I got to speak with Bennett and Baird last week. In the interview, they talked about the study, how fMRI really works, and what scientists have to do to make sure they can trust their own results.

Craig Bennett: We're not directly measuring activity in the brain. You'd need electrodes implanted in the brain itself for that. We're actually measuring the amount of magnetic disruption in the brain. We use a trick of how brain and body work. Oxygenated and deoxygenated blood have different magnetic properties.

Abigail Baird: If a brain region is doing a lot of work it's probobably going to be bringing in a lot of oxygen through increased blood flow. The premise is that if an area is working harder it will need more nutrients and oxygen and that will be delivered through the blood.

Using blood flow as measure of brain activity is reliable, but it's a very slow response. True brain activity happens when cells are communicating using neurotransmitters and electricity. Real, actual brain activity is measured with electrodes in the brain or someting like EEG that records electrical activity. The problem with doing that is that when you use EEG, you don't know exactly where the signal is coming from or what the signal means. fMRI presupposes that brain activity relies on oxygen but there's a 4-6 second delay because that's how long it takes for the call for more blood to go out. It's a slow response and in a way it's a sloppy response. We're assuming that there are more leftovers here in spot A then spot B, so there must be brain activity here and not there.

CB: The best description I've heard is that it's like coming up on thhe scene of a car accident and being able to tell what happened based on the skid marks. We have to try to interpret by the changes what was going on when the activity happened. It's a proxy.

AB: I'm so tired about hearing about "the brain lighting up". It makes it sound like you see lights in the head or something. That's not how the brain works. It suggests a fundamental misunderstanding of what fMRI results mean. Those beautiful colorful maps ... they're probability maps. They show the likelihood of activity happening in a given area, not proof of activity. According to our analysis, there's a higher likelihood of this region using more blood because we found more deoxygenated blood in this area. It's also correlational. Here's a time frame and the changes we'd expect, so we see which bits of brain correlate with that.

CB: We've had methods to look inside the brain of a living human for decades, and we've gotten quality science out of that method. What does fMRI add? The big thing is spatial location, you can say where in the brain activity is happening to a much greater degree. It's really mostly about that. But what that buys you is the ability to produce really pretty maps of the brain. You get a greyscale image with the colored spots that indicate what's significant. But that's not showing brain activity, it's showing a statistic. I drew a line in the sand and said these dots are the ones that crossed the line. It makes for drammatic and pretty presentation of data. If you have a page of jargon people will believe it at a certain level. But if you put a picture of the brain with active voxels [a three-dimensional pixel] people will believe it even more because a picture of the brain is next to it. We have a powerful tool and ability to create dramatic persuasive figures. And we can use it in improper ways.

CB: This is why we have to do tightly controlled experiments. To do it right, you'll take two conditions, almost exactly matched except for one critical thing. Some of the studies I really like are visual studies. I could show you the same stimulus, say a flashing circle of light, but I'd change the position of it. Whether it's inn the top third or the bottom third of your field of vision. Just by changing the position and comparing each position to each other you can see which parts of the brain are sensitive to each spot. That's a narrow study and a really good control.

AB: More than a couple papers have been sesationalistic. There have been comparisons of Republican and Democratic brains. That's ridiculous and it's a misuse of fMRI. It's not a specific enough question.

AB: In an fMRI study you have to stimulate the brain in some way. So what are you showing the brain in order to make distinction between Republicans and Democrats? Say it's pictures of people on welfare, and Democrats showed more activation in one area and Republicans in another. It doesn't actually tell you anything about Democrats and Republicans. Those results might tell you something about compassion. Or how we process compassion. But to say there are fundamental differences as a whole group between two groups of people, when there's so much variation within the group, it's just silly. I could get the same result ... find big differences ... with two groups of Democrats.

Remember, the brain doesn't just light up and those images are showing statistics, not all activity. If you see the same thing in several different studies, you can trust it more. But you should be suspect of one study of a handful of people, especially if the question wasn't specific enough and the researchers just went fishing to see what would happen. Also, what you're seeing is an average of the group, not each individual. You could have a group of 40 people and 39 out of the 40 show activity in one area, but that area might still get dropped from the final images because everybody didn't have it. So you need to consider the individuals, not just the group.

CB: In almost any experiment, but especially with MRI and fMRI, it's a noisy measure. There's all kinds of noise that gets entered into the signal. It'll pick up your own heart beating. We once had a lightbulb going bad in the scanner suite and it was introducing specific singal in our data set. You have to get enough data ... run the experiment enough times ... to separate signal from noise.

We're looking for variation in the magnetic field. With the salmon, fat will do that. Fatty tissue has a magnetic signal, but some areas of fatty tissue are more dense, and some less, so you'll see a differential. The salmon's brain was more fatty and that created more inherent variability. But it was just noise. It wasn't due to any actual activity but just happened to match our study design. Now, that's unlikely. But it just happened to happen. It's possible to find a false positive like that.

AB: We also saw activity outside the body of the salmon. The magnet itself has noise. It will always have noise. And if the threshold is low enough you're going to get that noise pattern matching up with your hypothesis.

AB: In most behavioral sciences and natural science, there's a certain cutoff level where we consider the things we've found significant or not. The gold standard is .01, less than a 1% chance that you're seeing something just by accident. Or a 99% chance that it's an actual difference. But, still, 1 out of 100 times you'd get that exact same result just by chance. We're also interested in data at the .5 level. Anything up to 10% we tend to call that a trend — something might be happening. That has held throughout history of psychology and neuroscience and it's pretty good. But we'd never had any tools that produced the magnitude of data that fMRI has. Instead of making comparisons between two groups of 40 people, you're making comparisons between 100,000 points in the brain and that .01 no longer says as much because you have so much more information to work with.

CB: Here's my analogy, if I give you a dart and say, "Try to hit the bullseye", you have some chance of hitting it. Your chance is not 0. But, depending on skill, you might hit more or less often. So you try the throw with one dart and hit on first throw, that's impressive. That's like finding a result. But if you only hit it once out of 100 tries, it's less impressive. In fMRI it's like having 60,000 darts you can throw. Some will hit the bullseye by chance and we need to try to correct for that. We tend to set a threshold and say anything over is legitimate and anything under is not. But what our team found is that in a surey of literature, between 25-40% of published papers were using an improper correction. You have a lot more chances of finding significance so you need to be more conservative of saying what is a legit result.

AB: So if you have a really specific hypothesis you can stick to the traditional numbers. But if you don't know what you're looking for and you just want to see "what lights up", then you're getting lots more chances to see things that could be just random. That's when you need to be more strict about what you consider real. And people aren't always as careful about that as they could be.

CB: Up to 40% of papers published in 2008 didn't do proper correction, so are there incorrect results in literature? Absolutely. Even if we correct perfectly you'll probably have 5% incorrect. There will always be false positives. But as a field we need to do as good a job as possible to release the best results we can. What we're saying is that it's not good for you, your study, or the field as a whole to not correct hard enough.

• You can read Craig Bennett and Abigail Baird's full paper online at the Journal of Serendipitous and Unexpected Results

• Read a story Alexis Madrigal wrote for Wired about this study in 2009

• Read blogger and neuroscientist Scicurious' article on the dead salmon study, published after the IgNobel announcement.

Genius science writer Ed Yong used to work for a cancer charity, so he's seen how the cancer research sausages get made. In a new post at Not Exactly Rocket Science, Ed takes you on a brief tour of the factory, explaining why even good data doesn't necessarily mean what you think it means.

The post is based around a new study that says 16.1% of all cancers worldwide are caused by infections. This statistic is talking about stuff like HPV—viruses and other infections that can prompt mutations in the cells they infect. Sometimes, those mutations propagate and become a tumor.

That statistic tells us that infections play a role in more cancers than most laypeople probably think, Ed says. It gives us an idea of the scale of the problem. But you have to be careful not to read too much into that 16.1%.

The latest paper tells us that 16.1% of cancers are attributable to infections. In 2006, a similar analysis concluded that 17.8% of cancers are attributable to infections. And in 1997, yet another study put the figure at 15.6%. If you didn’t know how the numbers were derived, you might think: Aha! A trend! The number of infection-related cancers was on the rise but then it went down again.

That’s wrong. All these studies relied on slightly different methods and different sets of data. The fact that the numbers vary tells us nothing about whether the problem of infection-related cancers has got ‘better’ or ‘worse’. (In this case, the estimates are actually pretty close, which is reassuring. I have seen ones that vary more wildly. Try looking for the number of cancers caused by alcohol or poor diets, if you want some examples).

And that's only one of the complications involved in understanding cancer statistics. You really should read Ed's entire post. After you do, a lot of apparent inconsistencies in cancer data will make a lot more sense to you. For instance: What about the cancers caused by radiation exposure?

I ran into some of these problems while researching Before the Lights Go Out, my book about electricity and the future of energy. The topic meant I had to spend some time dealing with the risks posed by nuclear energy. Specifically, people want to know what happens to the local population when a nuclear power plant melts down. How many people die? The problem: There's more than one legitimate answer to that question.

Take Chernobyl. There is not one, definitive number I can give you for how many people died because of the accident at the Chernobyl nuclear power plant. There've been, if I'm counting correctly, six different papers estimating how many people the radiation released during the accident will eventually kill. The various estimations range from 4000 to almost a million. But beyond checking each other's methodology—and there are some serious problems with the methodology used by the paper that estimated the highest death toll—it's really hard to say who is right and who is wrong.

If you read Ed's post, you'll get two good clues as to why that is:
First, statistics that show you how many cancer deaths were caused by x factor aren't produced by counting the numbers of dead cancer patients. Those statistics are based on data, assumptions, and computer models. Use different data sets, different models, or different assumptions and you will get different numbers.
Second, cancer isn't like a collapsing roof. If a beam falls on someone's head you can look at the autopsy and say, "This death was caused by this piece of wood." You don't have to take into account the hundreds of times that person might have bonked their head on a doorway or cabinet over the course of their life. It was clearly the beam that did them in. But there's usually more than one reason people get cancer. In fact, a certain percentage of the population will get cancer simply as a side effect of being alive. Add into that all the other risk factors that most of us are exposed to over the course of our lives and it becomes extremely difficult to tease apart a real, honest-to-god answer to the question, "What caused this specific person's specific cancer?"

Read Ed Yong's full post at Not Exactly Rocket Science

A cybercrime where profits are slim and competition is ruthless also offers simple explanations of facts that are otherwise puzzling. Credentials and stolen credit-card numbers are offered for sale at pennies on the dollar for the simple reason that they are hard to monetize. Cybercrime billionaires are hard to locate because there aren’t any. Few people know anyone who has lost substantial money because victims are far rarer than the exaggerated estimates would imply.

The authors frame cybercrime as a "tragedy of the commons," where the overfishing (overphishing) by crooks has reduced everyone's margins to nothing, making it hard graft indeed. Meanwhile, cybercrime estimates are subject to the same lobbynomics used to calculate losses from music downloading and profits from drug seizures:

Suppose we asked 5,000 people to report their cybercrime losses, which we will then extrapolate over a population of 200 million. Every dollar claimed gets multiplied by 40,000. A single individual who falsely claims $25,000 in losses adds a spurious $1 billion to the estimate. And since no one can claim negative losses, the error can't be canceled.

Cybercrime as a Tragedy of the Commons ]]> http://boingboing.net/2012/05/03/cybercrime-sucks-for-criminal.html/feed 2 http://boingboing.net/2012/04/18/why-the-dhss-pre-crime-biome.html http://boingboing.net/2012/04/18/why-the-dhss-pre-crime-biome.html#comments Wed, 18 Apr 2012 15:00:53 +0000 Cory Doctorow http://boingboing.net/?p=155205 The Atlantic, Alexander Furnas debunks the DHS's proposal for a "precrime" screening system that will attempt to predict which passengers are likely to commit crimes, and single those people out for additional screening.]]>
In The Atlantic, Alexander Furnas debunks the DHS's proposal for a "precrime" screening system that will attempt to predict which passengers are likely to commit crimes, and single those people out for additional screening. FAST (Future Attribute Screening Technology) "will remotely monitor physiological and behavioral cues, like elevated heart rate, eye movement, body temperature, facial patterns, and body language, and analyze these cues algorithmically for statistical aberrance in an attempt to identify people with nefarious intentions." They'll build the biometric "bad intentions" profile by asking experimental subjects to carry out bad deeds and monitoring their vital signs. It's a mess, scientifically, and it will falsely accuse millions of innocent people of planning terrorist attacks.

First, predictive software of this kind is undermined by a simple statistical problem known as the false-positive paradox. Any system designed to spot terrorists before they commit an act of terrorism is, necessarily, looking for a needle in a haystack. As the adage would suggest, it turns out that this is an incredibly difficult thing to do. Here is why: let's assume for a moment that 1 in 1,000,000 people is a terrorist about to commit a crime. Terrorists are actually probably much much more rare, or we would have a whole lot more acts of terrorism, given the daily throughput of the global transportation system. Now lets imagine the FAST algorithm correctly classifies 99.99 percent of observations -- an incredibly high rate of accuracy for any big data-based predictive model. Even with this unbelievable level of accuracy, the system would still falsely accuse 99 people of being terrorists for every one terrorist it finds. Given that none of these people would have actually committed a terrorist act yet distinguishing the innocent false positives from the guilty might be a non-trivial, and invasive task.

Of course FAST has nowhere near a 99.99 percent accuracy rate. I imagine much of the work being done here is classified, but a writeup in Nature reported that the first round of field tests had a 70 percent accuracy rate. From the available material it is difficult to determine exactly what this number means. There are a couple of ways to interpret this, since both the write-up and the DHS documentation (all pdfs) are unclear. This might mean that the current iteration of FAST correctly classifies 70 percent of people it observes -- which would produce false positives at an abysmal rate, given the rarity of terrorists in the population. The other way of interpreting this reported result is that FAST will call a terrorist a terrorist 70 percent of the time. This second option tells us nothing about the rate of false positives, but it would likely be quite high. In either case, it is likely that the false-positive paradox would be in full force for FAST, ensuring that any real terrorists identified are lost in a sea of falsely accused innocents.

Homeland Security's 'Pre-Crime' Screening Will Never Work

(Image: Brockhaus and Efron Encyclopedic Dictionary, a Creative Commons Attribution (2.0) image from double-m2's photostream) ]]> http://boingboing.net/2012/04/18/why-the-dhss-pre-crime-biome.html/feed 30 http://boingboing.net/2012/03/08/danish-trade-minister-and-acta.html http://boingboing.net/2012/03/08/danish-trade-minister-and-acta.html#comments Thu, 08 Mar 2012 10:16:28 +0000 Cory Doctorow http://boingboing.net/?p=147611

Here's a clip of a Danish TV show discussing ACTA, which Denmark has fiercely advocated in favor of. It starts with the head of a rightsholder society and the Danish trade minister quoting dodgy statistics about the extent and cost of piracy, and then demonstrates that these statistics are patently false, and finally, brings out those responsible for quoting them and gets them to admit their errors. Priceless.

You can see both the Danish Trade Minister and the head of a Danish music rights organization (and famous Danish musician) Ivan Pedersen appear on a TV show below (with English subtitles). On the show, a well-informed presenter focuses on how both of these ACTA defenders claimed that 95% of music downloaded in Denmark was unauthorized, and carefully shows how that's simply false -- and then gets both of the ACTA defenders to admit that the numbers were wrong.

Danish Trade Minister Apologizes For Using Bogus Industry Numbers To Support Pro-ACTA Argument ]]> http://boingboing.net/2012/03/08/danish-trade-minister-and-acta.html/feed 31 http://boingboing.net/2012/02/08/facebooks-funny-accounting-h.html http://boingboing.net/2012/02/08/facebooks-funny-accounting-h.html#comments Wed, 08 Feb 2012 17:59:38 +0000 Cory Doctorow http://boingboing.net/?p=142758 NYT's Andrew Ross Sorkin quotes Barry Ritholtz's digging into how Facebook's IPO documents define "active" users and finds that many of them may never visit the site.]]> The NYT's Andrew Ross Sorkin quotes Barry Ritholtz's digging into how Facebook's IPO documents define "active" users and finds that many of them may never visit the site. Facebook counts you as "active" if your only involvement with the service is setting it up to republish your Twitter feed, or if you click "Like" buttons but never log in to the actual service. This should matter to investors, since Facebook earns no advertising revenue from those users, though it may earn some other income by reselling the private details of their browsing habits as gleaned from its tracking cookies.

In other words, every time you press the “Like” button on NFL.com, for example, you’re an “active user” of Facebook. Perhaps you share a Twitter message on your Facebook account? That would make you an active Facebook user, too. Have you ever shared music on Spotify with a friend? You’re an active Facebook user. If you’ve logged into Huffington Post using your Facebook account and left a comment on the site — and your comment was automatically shared on Facebook — you, too, are an “active user” even though you’ve never actually spent any time on facebook.com.

“Think of what this means in terms of monetizing their ‘daily users,’ ” Barry Ritholtz, the chief executive and director for equity research for Fusion IQ, wrote on his blog. “If they click a ‘like’ button but do not go to Facebook that day, they cannot be marketed to, they do not see any advertising, they cannot be sold any goods or services. All they did was take advantage of FB’s extensive infrastructure to tell their FB friends (who may or may not see what they did) that they liked something online. Period.”

Those Millions on Facebook? Some May Not Actually Visit (via Memex 1.1) ]]> http://boingboing.net/2012/02/08/facebooks-funny-accounting-h.html/feed 21 http://boingboing.net/2012/01/24/how-deadly-is-bird-flu.html http://boingboing.net/2012/01/24/how-deadly-is-bird-flu.html#comments Wed, 25 Jan 2012 02:02:26 +0000 Maggie Koerth-Baker http://boingboing.net/?p=140509 The Centers for Disease Control and Prevention, and the World Health Organization, say that H5N1 bird flu kills some 60% of the human beings it manages to infect.]]>

The Centers for Disease Control and Prevention, and the World Health Organization, say that H5N1 bird flu kills some 60% of the human beings it manages to infect. Basically, it hasn't infected many people—because it can't be spread from person to person—but most of the people it does infect die.

But this might not be the full story.

After I posted a summary of the current controversies surrounding H5N1 research, I got an interesting email from Vincent Racaniello, a professor of microbiology at Columbia University Medical Center. Racaniello points out that the 60% death rate statistics are based on people who show up at hospitals with serious symptoms of infection. So far, there've only been about 600 cases. And, yes, about 60% of them have died.

However, they don't necessarily represent everybody who has contracted H5N1.

A death rate is only as good as statistics on the rate of infection. If you've got an inaccurate count of the number of people infected, your death rate is going to be wrong. And there's some evidence that might be the case with H5N1.

In a recent study of rural Thai villagers, sera from 800 individuals were collected and analyzed for antibodies against several avian influenza viruses, including H5N1, by hemagglutination-inhibition and neutralization assays. The results indicate that 73 participants (9.1%) had antibody titers against one of two different H5N1 strains. The authors conclude that ‘people in rural central Thailand may have experienced subclinical avian influenza virus infections’. A subclinical infection is one without apparent signs of illness.

If 9% of the rural Asian population has been subclinically infected with avian H5N1 influenza virus strains, it would dramatically change our view of the pathogenicity of the virus. Extensive serological studies must be done to determine the extent of human infection with avian H5N1 influenza viruses. Until we know how many individuals are infected with avian influenza H5N1, we must refrain from making dire conclusions about the pathogenicity of the virus.

At Bloomberg Business Week, Vali Chandrasekaran makes me incredibly happy by creating a series of six infographics demonstrating the ridiculous connections you can make when you start confusing correlation and causation. Did a conspiracy of baby Avas cause the U.S. housing market to implode? Was Michele Bachmann's candidacy doomed by the end of Staten Island Cakes? Are scientists raising the global average temperature in order to increase their own research funding? Find out here!

Biostatistics Ryan Gosling will look deeply into your eyes and ask about your p-value.

What are the odds that you, as an individual, exist? Pretty good, you'd guess, since you're sitting right here reading this. But, in an abstract sense, the chances that you exist are really rather slim. In fact, once you see the full infographic, put together by futurist and designer Sofya Yampolsky of Visual.ly, I'm sure you'll be much more skeptical of your existence.

The infographic is based on this post by Dr. Ali Binazir.

This is a big number. A very big number. Especially since the same poll question, when asked in France (where the motion picture lobby has succeeded in passing a "disconnect anyone we don't like from the Internet" law) showed that only four percent of downloaders changed their habits out of fear of detection.

No, it's not that Australians are easily frightened. Rather, the Intellectual Property Awareness Foundation (an "independent" firm that lists the MPAA on its board and has no visible clients apart from the entertainment industry) included responses from people who don't download in its poll -- that is, they lumped in the very small number of people (zero, possibly) who said, "I download, and this would make me stop" with the very large number of people who said, "I don't download, but, well, hypothetically, if I did, this might make me stop."

If 72 percent say they would stop sharing after a warning, then 28 percent didn’t agree with this statement. And since only 22 percent of the people said they used file-sharing software in 2011 (the only people who would be affected by a three strikes system), this means that warnings from ISPs wouldn’t even deter people who aren’t the target of this system in the first place.

Or put differently, it could very well be that none of the 22 percent file-sharers indicated that they would stop doing so when notified by their ISP.

Now that’s an entirely different conclusion isn’t it?

Twelve patients who served as their own controls wore compression stockings for a week and then no stockings for a week alternating. The stockings lowered the amount of fluid in the neck by 60%, a statistically significant difference. So far, so good.

This resulted in another highly statistically significant finding, which was a 36% reduction in episodes of apnea [cessation of breathing] and hypopnea [inadequate breathing]. Sounds good, right? The problem is that the average number of episodes of apnea/hypopnea decreased from 48 per hour to 31 per hour. Patients experiencing more than 30 episodes of apnea/hypopnea per hour are classified as having severe obstructive sleep apnea. This means that the treatment only put the patients in the low range of severe obstructive sleep apnea. They still would require maximum therapy.

The Sun said: "Police have charged nearly 150 people after violent anarchists hijacked the anti-cuts demo and brought terror to London's streets." The Guardian republished a Press Association report, headlined: "Cuts protest violence: 149 people charged". And from the locals, for example, the Manchester Evening News carried "Boy, 17, from Manchester among 149 charged over violence after anti-cuts march".

In reality, a dozen of these charges related to violence, while 138 are people who were involved in an apparently peaceful occupation of Fortnum and Masons organised by UKUncut, who campaign on tax avoidance.

You will have your own view on whether people should be arrested and charged for standing in a shop as an act of protest. But describing these 150 people as "violent anarchists... who brought terror to London's streets" is not just misleading: it also makes the police look over 12 times more effective than they really were at charging people who perpetrated acts of violence.