Will sprinters one day break the sound barrier? Do Olympic athletes win more medals if they wear red? And can a simple formula predict happiness?
While those questions may sound absurd, various studies have found away to prove them true through statistical manipulation of numbers and data. The tendency of academics, politicians and pundits to generate such numerical falsehoods from data — and the tendency of the public to believe the results — is a phenomenon cleverly explored in the new book “Proofiness: The Dark Arts of Mathematical Deception,” by Charles Seife.
Mr. Seife, a writer and professor of journalism at New York University, makes a compelling case that numbers have a unique hold onthe human mind, and that we are routinely bamboozled by phony data, bogus statistics and bad math. I recently spoke with Mr. Seife, whose work has appeared in The New York Times, The Economist and elsewhere, about the role that proofiness plays in health and medical research. Here’s our conversation.
What is “proofiness?” [這是這本書的作者自創的字]
It’s the mathematical analog of Stephen Colbert’s “truthiness.”It’s using numbers to prove what you know in your heart is true, even when you know it’s not. Numbers have a particular ability to fool us. It’s using that ability to turn nonsense into something that is believable with numbers.
Was there any particular case or event that inspired you to dedicate an entire book to this phenomenon?
I’ve been gathering thread about the book since my college days. I was always a split personality, studying to be a mathematician but drawn to writing and journalism. One of the things that drove me to journalism was my annoyance at how innumerate the media seemed to be. We just don’t seem to be able to handle numbers. I wound up picking out little stories where people were deceived by numbers. I thoughti nitially it would be a fun little book about the silly ways people’s thinking can go wrong, but it turned into something much more sinister— the idea that mathematical deception is playing a large role in the way our society was run.
You write about the fact that numbers have enormous power over our thinking. Why is that?
From school days, we are trained to treat numbers as platonic, perfect objects. They are the closest we get to absolute truth. Two plus two always equals four. Numbers in the abstract are pure, perfect creatures. The numbers we deal with in the real world are different. They’re created by humans. And we humans are fallible. Our measurements have errors. Our research misses stuff, and we lie sometimes. The numbers we create aren’t perfect platonic ideals. They are mixed with falsehood, but we don’t recognize that.
In the book you make the point that bad math can undermine boththe political and judicial process. How can it affect medicine and health?
One of the things our minds are designed to do is pick up patterns. If you eat a bit of bad shrimp and get sick, your mind makes that association and you get an aversion to that food. We are extraordinary pattern-matchers. Anytime there is something that is happening, we tryto find a cause. But sometimes in medicine, sometimes things are absolutely random. Our minds don’t accept that. We must find a cause for every effect.
A really good example is the autism issue. Whenever a parent has achild who ends up being autistic, the parent more than likely says,“What caused it? How did it happen? Is there anything I could have done differently?” This is part of the reason why people have been so down on the M.M.R. vaccine, because that seems like a proximate cause. It’s something that usually happened shortly before the autism symptoms appeared. So our minds immediately leap to the fact that the vaccine causes autism, when in fact the evidence is strong that there is no link between the M.M.R. vaccine or any other vaccines and autism.
MMR vaccine n. 預防麻疹 (measles)、腮腺炎 (mumps)、德國麻疹 (rubella) 的疫苗。一直以來，人們就接種這種疫苗會不會導致兒童自閉症等疾病產生了爭論。
Q.In the chapter titled “Rorschach’s Demon,” you coin the term “causuistry.” Can you explain the word?
[causuistry 是這本書的作者自創的字，可能是結合cause casuistry 而來]
casuistry n. a way of solving moral or legal problems by using clever arguments that may be false 詭辯（指用似是而非的論點解決倫理或法律問題）
Casuistry is using bogus arguments through seemingly sound principles. Causuistry is my shorthand for wrongly implying causation. The issue is that inmedicine or any other field of study, it’s really easy to show that two things are linked in some manner. Something rises, something else falls. As energy consumption rises, so does life expectancy. However, it’s a fallacy to say without other evidence that one is causing the other. You can’t say building more power plants will cause us to live longer. In fact, what’s going on in this example, there is an underlying cause affecting both. The more technological a society is, the more power plants it has, the longer its people live. It’s very easy for a researcher to mistake a correlation for causation. It’s very hard to show that one thing causes the other.
Can you give me another example of causuistry?
A number of years ago there was a study that showed the higher yourcredit card debt, the worse your health. The conclusion seemed to be“Don’t carry a balance on your credit card, otherwise you’ll get sick.”It’s probably just the opposite. People who are sick are running upmedical bills, missing work or maybe have lost their jobs. It’s notthat credit cards cause bad health. It’s that bad health causes unpaidbills on your credit card.
Another word you use is “randumbness.” Can you explain it?
We’re hard wired to reject the idea that there’s no reason for something happening. This is how Las Vegas makes its money. You’ll have people at the craps table thinking they’re set for a winning streak because they’ve been losing. And you’ll have people who have been winning so they think they’ll keep winning. Neither is true. These events are completely random. The universe doesn’t care if you’ve been winning or losing, but our minds see these pattens we think we can exploit, and this leads us to phony beliefs.
Randumbness is our stupidity about true randomness. We are unable to accept the fact that there’s not a pattern in certain things, so we project our own beliefs and patterns on data, which is pattern-free. In the journal Nature a few years ago, some researcher sanalyzed a number of Olympic sports and saw that people who wore red were winning more than people who wore blue. They concluded that red confers an advantage. This is nonsense. It was a random event. In the 2008 Beijing Olympics, you can analyze the same events in the same way, and you find just the opposite. People who wore blue had a statistically significant advantage over people who wore red.
One of the tools researchers use to find patterns in data is the regression analysis. Why do you call this “regression to the moon?”
A regression analysis is a tool for taking a set of data, a collection of points, and making sense of it with a formula. It’s a powerful technique because it allows you to present data in terms of things you think are relevant.
A good example is in economics. If you think elections are affectedby the inflation rate and G.D.P. and the unemployment rate, you turn all of these things into a regression model, and you come up with a formula that predicts the president based on these variables. The problem is that if your initial assumptions don’t have a basis in reality, then it’s going to come up with an answer that makes it look like there’s a connection when in fact there isn’t. This straight regression analysis assumes everything is linear, that there’s a very simple equation that relates to these variables. But the real world isn’t linear. It’s complex.
How are we harmed by “causuistry,” “randumbness” and “regression to the moon?”
There’s harm in bad research, and there’s harm in biased research. This is a problem the medical research community has been dealing with. We tend to think things work better or work at all when they in fact don’t. It’s undermining not just the information that doctors and consumers use, but also the scientific process in general. As people recognize that scientific studies are often not as objective and scientific as they seem, that they include biases and bad numbers, it undermines the credibility of an evidence-based medicine system.
So should we be skeptical of all scientific research? Can we believe anything we read?
I think the biggest thing to take home is that you have the right to question research, the right to think this number doesn’t make sense. I think the best thing to do is if something doesn’t make sense to you, you’re going to learn something by examining it. Sniff it. Figure out where it’s coming from. A little degree of skepticism is usually warranted, especially when there is a number that doesn’t make sense.
http://well.blogs.nytimes.com/2010/10/29/the-dark-art-of-statistical-deception
iPhone用戶 性伴侶平均逾10人
2010年08月12日蘋果日報
[蔡惠如、蔡文英╱綜合報導】蘋果智慧手機iPhone讓人更有性吸引力？美國網路調查指，相較於用其他智慧手機者，拿iPhone者，無論男女，性伴侶人數都比較多，以三十歲iPhone用戶為例，女性平均有十二點三名性伴侶，男性則有十名，高於黑莓機（BlackBerry）和Android平台手機使用戶的性伴侶人數。
「跟陌生人易切入話題」
美國《PC雜誌》前天報導，OKCupid交友網站針對九千七百八十五名十八至四十歲的智慧手機用戶進行的網路調查發現，iPhone用戶性生活最活躍，Android手機用戶男女關係較穩定，性伴侶人數少於iPhone和黑莓機用戶。但由於是網站民調，受訪者不見得絕對誠實。
iPhone 3GS用戶葉玟芳說：「因iPhone有app store平台可下載許多程式，跟陌生人較好切入話題，能交到很多朋友，但增加性伴侶應該跟用iPhone無關，跟人比較有關吧。」也用iPhone的上班族蔡旻融則說：「用iPhone給人感覺較時尚，應可為形象加分。」
使用hTC Android平台手機的民眾陳昭民說：「不覺得使用手機跟性伴侶的多寡有什麼關係。」用黑莓機的民眾陳啟綱說：「現在iPhone太氾濫了，沒什麼特別，不一定為形象加分，受歡迎應是個人特質，跟手機無關。」
http://tw.nextmedia.com/applenews/article/art_id/32731681/IssueID/20100812
Phys Ed: Can Running Actually Help Your Knees?
By Gretchen Reynolds
August 11, 2009, 11:59pm
You can’t be a runner past the age of 40, as I am, without hearing that running will ruin your knees, by which doomsayers usually mean that we’ll develop “degeneration of the cartilage in the kneecap, which reduces its shock-absorbing capacity,” says Ross Tucker, a physiologist in South Africa and co-author of the new book “The Runner’s Body: How the Latest Exercise Science Can Help You Run Stronger, Longer and Faster.” In other words, we’ll be afflicted with arthritis.
It’s not an unreasonable supposition; other sports have been linked with early-onset arthritis in knees. In a British study, almost half of the middle-aged, formerly elite soccer players were found to have crippling, bone-on-bone arthritis in at least one knee. Former weight lifters also have a high incidence of the condition, as do retired N.F.L. players.
But despite entrenched mythology to the contrary, runners don’t seem prone to degenerating knees. An important 2008 study, this one from Stanford University, followed middle-aged, longtime distance runners (not necessarily marathoners) for nearly 20 years, beginning in 1984, when most were in their 50s or 60s. At that time, 6.7 percent of the runners had creaky, mildly arthritic knees, while none of an age-matched control group did. After 20 years, however, the runners’ knees were healthier; only 20 percent showed arthritic changes, versus 32 percent of the control group’s knees. Barely 2 percent of the runners’ knees were severely arthritic, while almost 10 percent of the control group’s were. “We were quite surprised,” says Eliza Chakravarty, an assistant professor at the Stanford University School of Medicine and lead author of the study. “Our hypothesis going in had been that runners, because of the repetitive pounding, would develop more frequent and more severe arthritis.”
Instead, recent evidence suggests that running may actually shield somewhat against arthritis, in part because the knee develops a kind of motion groove. A group of engineers and doctors at Stanford published a study in the February issue of The Journal of Bone and Joint Surgery that showed that by moving and loading your knee joint, as you do when walking or running, you “condition” your cartilage to the load. It grows accustomed to those particular movements. You can run for miles, decades, a lifetime, without harming it. But if this exquisite balance is disturbed, usually by an injury, the loading mechanisms shift, the moving parts of the knee are no longer in their accustomed alignment and a “degenerative pathway” seems to open. The cartilage, like an unbalanced tire, wears away. Pain, tissue disintegration and, eventually, arthritis can follow.
So, the best way to ensure that your knees aren’t hurt by running is not to hurt them in the first place. “The biggest predictor of injury is previous injury,” Tucker says, and one of the best deterrents against a first (or subsequent) knee injury is targeted strength training. “The hip stabilizers, quads, hamstrings and core must all be strong enough. As soon as there is weakness, some other muscle or joint must take over, and that’s when injuries happen.”
If you’ve injured your knee in the past, particularly if you’ve ever torn an A.C.L. (an injury that, in the Stanford gait study, was closely associated with misalignment and cartilage degeneration), talk to your physician before running. But for most runners, the scientific observations of Chakravarty will ring true. “What struck me,” she says, “is that the runners we studied were still running, well into their 70s and 80s.” They weren’t running far, she says. They weren’t running frequently. They averaged perhaps 90 minutes a week. “But they were still running.”
http://well.blogs.nytimes.com/2009/08/11/phys-ed-can-running-actually-help-your-knees
The stories were taken from The New York Times and Apple Daily, which were not involved with nor endorsed the production of this blog. The copyright remains with each article's original owners.