Thought I’d post a followup report and let Tom focus on the book over the holiday weekend.
As I reported here, after reading “Born to Run” last year, I got interested in the idea of people being designed to run. Even old, fat people. So, with the encouragement of a couple of my coworkers,
I signed up for this year’s Abe’s Army training program, which consists of weekly organized small group runs with experienced runners, along with some personal miles logged, culminating 13 weeks later in participation in the 10k Abe’s Amble, which is run the last Sunday of the Illinois State Fair. The race starts inside the fairgrounds at 7:30 am, heads out of the fair, through nearby Lincoln Park, out the back of the park, through the (hilly) cemetery where Lincoln is buried, then back. BTW, for us non-metric types, 10k is 6.2 miles.
I missed a couple of the long group runs the last couple of weeks, but did running on my own, and I’d also starting pedaling the 2 miles to the office every day, so I felt like I was ready.
As an added bonus, it turned out that whoever organized the race this year must have some MAJOR contacts somewhere, because Saturday night Central Illinois broke out of a weeks-long string of 90-100 degree weather and we ended up with 65 degrees and overcast for the start of the race.
Here’s most of our group (Blue 2) just before the race started.
I’m the bright yellow one in the back with the funny “running shoes.”
About those — I’d been doing my personal runs in the Huaraches all along, but had been doing the group runs in a pair of Lems shoes that are zero-drop, barefoot shoes but look like running shoes — just to blend in a bit (the ones in the pic at the top of this post). I wore the Huaraches to the last practice run (3 miles), and when I walked up the trainers looked at my feet, then up at me, and said “so you’re not running tonight?” I explained to them that they were structurally no different than the ones I’d been wearing to the group runs. They were interested, asked about injuries, etc., but very cool about it.
When we got to one of the water stops that are set up around the training course runs, someone from another group who’d seen my footgear came up asked “how are your feet feeling in those?” I said “great – I’ve been running this way all along.” She said she hadn’t thought people could run like that. I replied that “really, we spent thousands of years being designed to run like this.” She said yes, that made sense, but “I see too many people with ankle and knees problems” (I believe she’s in the medical arena); to which I replied, “and I bet they all wear running shoes, right?” She smiled a bit at that.
Anyway, my goal all along had been to run the race in the Huaraches, and the last practice run showed me that it wouldn’t be a problem.
So off we all went — the Abe’s Army program had around 150 people, but there were nearly 650 participants for the Amble. I ran with a buddy from my group (they guy on the left in the group pic), and we decided to keep using our training protocol of 5:1 intervals for the race — run 5 minutes, walk 1 minute, repeat, until you cross the finish line.
We moved towards the back third of the pack at the starting line so we wouldn’t be in the way of the real competitors, but be ahead of the walkers and dedicated slowpokes. Here’s me as I get past the starting gate…
(I don’t really have to go to the bathroom — that’s just the way my shorts bunched up!)
At any rate, I was able to maintain a blistering 13:10 min/mile pace (1:21:41.4 final time). I even had a bit of gas left in the tank for the finish and sprinted the last 100 yards. Of course, many people would mistake my sprinting for “strenuous jog,” but I still felt really good about it — way better than it looks like I felt:
In the final standings, I whipped 84 of the other folks’ butts (including most, but not all of the Olympic walkers and almost everyone over 70), and had the other 559 in front of me looking over their shoulders.
Well, maybe not all of them. The mutant who won (this guy, Bryan Glass:)
(5:21 min/mile; 33:09.4 MINUTES) blasted past my buddy and me going the other way when we were approaching the 2 mile mark, so he’d already covered over 4 miles. He didn’t have to look over his shoulder — he could’ve seen me coming from two miles away!
Actually, calling Mr. Glass a mutant is a disservice. I’m sure he’s got a good set of genes for running, but nobody can do that without training and focus beyond my imagination. He probably could catch his dinner ala “Born to Run.”
Me, I’m not selling my guns yet.
Four minutes behind him (and 44 minutes in front of me) was the first woman over the line. One of the interesting points in “Born to Run” was that the longer the distance, the closer women are to matching men.
It was a great experience, and it’s fired up my motivation to keep my activity level elevated. My running buddy and I are going to keep doing weekly runs; we’ve signed up for a 2 mile moonlight fun run/4 mile trail bike race in a couple of weeks; I ran 5k last weekend on vacation in Apple Canyon , IL (ALL hills!); I’m back doing resistance training once a week for the first time since my knee surgery last year; I’m biking to work; and I’m thinking of trying some swimming in the mornings at the local public indoor pool.
And besides all that, I got one of those “thanks for taking part” ribbons like Tom mentioned in his last Farm Report!
I’m a big fan of the hog (when they’re not smacking me around in a chute, that is), but I had no idea they’re this useful:
When we tuck into a bacon sandwich, few of us wonder what has happened to the other parts of the pig whose life has been sacrificed so we can enjoy a juicy breakfast.
But one inquisitive writer set out to trace where all the body parts of one porker ended up.
Christein Meindertsma, 29, said: ‘Like most people, I had little idea of what happens to a pig after it leaves the abattoir so I decided to try to find out. I approached a pig farmer friend who agreed let me follow one of his animals.’
Identified by its yellow ear tag number, 05049, her pig trail ended with her identifying an incredible 185 different uses to which it was put – from the manufacture of sweets and shampoo, to bread, body lotion, beer and bullets.
Virtually nothing in a pig goes to waste. The snout from Pig 05049 became a deep-fried dog snack, while pig ears are sometimes used for chemical weapon testing due to their similarity to human tissue.
Tattoo artists even buy sections of pig skin to practise their craft on due to its similarity to human skin, while it is occasionally used with burns patients for the same reason.
I’m starting to feel a bit chagrined that all we got from our hogs was 500 pounds of meat. I could have been practicing to become a tattoo artist while covering myself with body lotion, drinking a beer, and firing some bullets at a loaf of bread.
A team of Harvard scientists has paved the way for a deadly laser pig weapon by demonstrating that, with a little encouragement, pig fat cells can be made to lase.
According to MIT Technology Review, Seok Hyun Yun and Matjaž Humar stimulated spheres of fat inside porcine cells with an optical fibre, causing them to emit laser light.
And here I thought my belly was glowing because I’m happy.
Handily, pig cells contain “nearly perfectly spherical” fat balls, which are conducive to lasing by resonance when supplied with a suitable light source. The team has also cheated the effect by injecting oil droplets into other cells.
Seok Hyun Yun, lead author of the report which appears in Nature Photonics, reckons an ultimate use of his work might be to deploy “intracellular microlasers as research tools, sensors, or perhaps as part of a drug treatment”.
Drug treatment, my foot. Let’s put all the research dollars into that deadly laser pig weapon. Imagine if we have troops overseas in some future war:
“Achmed, what’s that smell coming from the American lines? Is it …?”
“Yes! I believe it’s BACON! Run! Run before they turn the pig-laser on us!”
And if would-be intruders are scared away from my house by the aroma of saugage, that’s fine by me.
Eggs With My Pig Laser, Please
In my Science For Smart People speech, I mentioned that when some researchers find a correlation in an observational study, they assume they’re looking at cause and effect. I gave the example of a meta-analysis which prompted the lead researcher to announce to the media, “The studies showed a significant increase of new onset diabetes with regular egg consumption.”
Sure sounds like cause and effect, doesn’t it? Based on other interviews, that’s indeed what the researcher believes. But of course, if eggs actually caused diabetes, we wouldn’t see observational studies like this one:
Men who ate more than five eggs a week had a lower risk of developing type 2 diabetes than men who ate about one a week only, according to researchers in Finland.
In a study with an average follow-up of almost 20 years with 2,332 participants, researchers noticed that those in the highest quartile for egg intake had a lower risk of developing diabetes than those in the lowest quartile when cholesterol and other factors were controlled for.
Yunsheng Ma, MD, an associate professor of medicine at the University of Massachusetts Medical School, said in an email to MedPage Today that the study “provides welcome news to support the 2015 dietary guidelines, which are expected to drop the limit of egg consumption for blood cholesterol concerns.”
Ma said that he was aware of six studies that examined egg consumption and diabetes. One showed an increased risk, he said, and the other five showed no association. “So these results are not in line with other findings,” wrote Ma.
So here’s the official score in Observational Study Stadium: one study shows a higher risk of diabetes with higher egg consumption, one shows a lower risk, and five show no association at all. That means there’s no cause-and-effect relationship, period. Any good science teacher could tell you that.
Iowa high school science teacher John Cisna weighed 280 pounds and wore a size 51 pants.
Then he started eating at McDonalds. Every meal. Every day. For 180 days.
By the end of his experiment, Cisna was down to a relatively svelte 220 and could slip into a size 36.
Unlike me, Cisna didn’t embrace a high-fat diet:
Cisna left it up to his students to plan his daily menus, with the stipulation that he could not eat more than 2,000 calories a day and had to stay within the FDA’s recommended daily allowances for fat, sugar, protein, carbohydrates and other nutrients.
I much prefer my “@#$% the government recommendations” diet. But I definitely enjoyed Cisna’s comments about Super Size Me:
“As a science teacher, I would never show ‘Super Size Me’ because when I watched that, I never saw the educational value in that,” Cisna said. “I mean, a guy eats uncontrollable amounts of food, stops exercising, and the whole world is surprised he puts on weight?’
“What I’m not proud about is probably 70 to 80 percent of my colleagues across the United States still show ‘Super Size Me’ in their health class or their biology class. I don’t get it.”
I get it. They like the anti-McDonald’s message, so they toss critical thinking out the window … assuming they had any critical-thinking skills to toss.
It’s 2015 … So Let’s See How the ‘90s Viewed the ‘60s
I never watched the TV show Quantum Leap, but a reader sent me a link to this YouTube clip. It’s part of an episode in which the main character visits his parents in 1969. Skip ahead to the 12-minute mark:
The episode aired in 1990. That’s right about when arterycloggingsaturatedfat! hysteria was in full swing. The main character goes back in time and is horrified by all the fat and cholesterol his father is eating. Now we can go back in time and be horrified by the fact that the main character is horrified.
Junior was right about one thing, though: Dear Old Dad needs to stop smoking.
If I could go back in time, I wouldn’t tell my dad to stop eating eggs and butter. I’d tell him to give up sugar and stop taking those @#$%ing statins. His 81st birthday would have been tomorrow, and man, I wish I could call him up, rib him about getting old, then wait for one of his witty comebacks.
It’s 2015 … So Everything Good Must Be Candy
This isn’t from a news item. It’s something I’ve noticed in a handful of TV commercials: vitamins and even fiber tablets now come in the form of gummies– for adults. I didn’t find a commercial online, but I did find this:
So apparently some people won’t take vitamins unless they taste like candy. If that’s not a sad comment on our dietary habits, I don’t know what is.
Must be a function of my age … as of a couple of days ago, I was still one of the few people on the planet who had never heard the song “All About That Bass.” What can I say? I’m old and I rarely listen to pop music. When I create our family end-of-the-year DVDs, I have to ask my daughters to suggest songs for the music videos. Otherwise, every DVD would be like a tribute to the golden oldies.
I was familiar with the song’s melody because Sara took a liking to a YouTube parody about ancient Greece, which was amusing because … well, heck, rather than describe it, I’ll embed the video:
The tune got stuck in my head, probably because Sara has been creating instant parodies of her own to comment on various situations. For example, after the hundredth or so time Chareva mentioned elderberry bushes, Sara began singing, “Because she’s all about that bush, ’bout that bush — elderberry. She’s all about that bush, ’bout that bush — elderberry.”
So a couple of days ago, I decided to look for the original song on iTunes. I liked what I heard, so I listened to samples of Meghan Trainor’s other songs. I liked them too, so I bought the iTunes album. Then later in the day, while taking a work break, it finally occurred to me to check if Trainor had produced a music video of “All About That Bass.”
Wow, she sure did — and it’s racked up nearly a billion views. That’s billion … with a b. Here it is, in case (like me) you’ve been living under an age-induced rock and haven’t seen it:
I freakin’ love this thing. The lyrics, the melody, the beat, the harmony, the instrumentation, all of it. And I love the body-acceptance message the video puts out there. That’s a message Chareva and I want to include in the book we’re producing for kids – perhaps the closing message.
Yes, you can improve your body composition with a good diet and the right kind of exercise. But most of us will never look like jocks or models, no matter what we do. I spent much of my early life feeling ashamed of the skinny, weak arms and legs extending from my fat-bellied body, complete with boy-boobs. That shame was a waste. A complete and utter waste.
I sometimes wish I could go back in time and have a long talk with that kid. I’d let him cry on my shoulder for awhile, then explain to him what actually matters in life – and it’s not the shape of his body. I can’t do that, so I’ll have a talk with kids who read the book. I want them to know they can’t compare themselves to people who were born to look like athletes or models. I want them to understand what a mistake it is to think, “Someday, with enough work and sacrifice, I’ll look like that – and then I’ll be happy.”
Anyway, I’ve listened to the song and chuckled my way through the video several times, enjoying Trainor’s message of “Every inch of you is perfect from the bottom to the top.”
Then today, I read some of the YouTube comments. I’m sorry to say there’s a sizable contingent of morons and ignoramuses among the millions of people who’ve seen the video. Here are some samples, in all their grammatically-challenged glory:
why cant you make people feel bad about the body, if it isnt any permanant disability where they cant do anything about it? fatties are fat because they cant spend enough time to exercise and cant refrain themselves from eating chunk loads of junk food its their fault.
Nice to finally hear from people who have carefully studied the issue … although I could swear I recently read a journal article that concluded: The commonly held belief that obese individuals can ameliorate their condition by simply deciding to eat less and exercise more is at odds with compelling scientific evidence.
This over-autotuned,IQ-lowering,grotesque shit made by someone who looks like a chubby 40-yo tranny – almost a BILLION??? Gotta be shittin me…
If this is an IQ-lowering video, then my guess is that you’ve already watched it at least a hundred times. Best stop now … before you can’t tell the difference between, say, Tom Brady and a 40-year-old tranny.
This is just wrong. Self-criticism is crucial for surviving. Now she promotes being fat. What comes afterwards ? Will she be responsible for rising levels of deaths because of diabetes and heart strokes? Don’t think so.
Yes, that’s what prevents fat people people from being thin: they don’t criticize themselves enough.
F***ing fatass ugly bitch and what a f***ing stupid song.
Something tells me Trainor will manage to have a stellar career despite your opinion. Perhaps the “something” is the almost-billion views.
So where’s the song for short men? Oh wait, this fat bitch probably has a 6 foot minimum requirement like all other women and doesn’t see the hypocrisy. You can’t grow taller, but you can burn off that fat ass bitch.
That’s the first thought that occurred to me as well: What, no lyrics praising short men?!! Clearly, Trainor only dates tall men, like all other women! What a hypocrite! I’m not six feet tall either, so maybe you and I should meet for drinks in a pub someday and share our feelings about women who don’t write songs for short men and other height bigots. I’ll bring a booster seat for you.
Once a year in the US, UK, and Canada they discover a fat chick that is able to sing and she’s like the hottest thing for about 2 months telling everyone how great it is to be fat and you should be proud of being fat. Enjoy your diabetes and heart surgery. No worries, Megan says it’s OK.
Dang, maybe I need to get stronger glasses. I’ve watched the video several times, and I haven’t spotted the fat chick yet. Is she hiding behind those dancers with the big leg muscles?
Nobody likes fat bitches stop trying to convince yourself otherwise.
No worries, sir. I’m sure there are still plenty of women in the world who meet your standards — and with that attitude of yours, you’ll no doubt make one of them a fine husband.
Okay, you get the idea. There were also laughable complaints that Trainor is insulting thin girls and and saying they’re not sexy, which will make them feel bad about themselves. Oh yeah, huge risk there. Given the current culture, I’m reasonably sure thin women won’t be suffering from size-related self-esteem issues any time in the next decade, no matter what lyrics Trainor writes for her songs.
If you’ve listened to a few podcasts where I was the interview guest, you probably know what eventually became Fat Head started out in my mind as a short piece on how we treat fat people in America. They’re perhaps the only remaining group you can make a target of nasty, bigoted remarks without being run out of town. It’s their fault, they’re disgusting, they did it to themselves, they could be thin if they wanted to, blah-blah-blah. Some of the YouTube comments prove the point rather clearly.
But a music video doesn’t draw nearly a billion views unless it strikes a chord with millions and millions of people. I believe this one struck a chord for a good reason.
In comments for my previous post about regression to the mean, someone asked a perfectly logical question: yes, a study subject’s cholesterol might be spiking on the day he’s screened, which means he’s enrolled in study of a cholesterol-lowering drug even though he doesn’t actually have high cholesterol. But doesn’t it work in reverse as well? Wouldn’t that occurrence of random chance be offset by someone whose cholesterol happened to be lower than usual on screening day?
The short answer is: no, the two random variations don’t offset. Let’s assume the cutoff for being enrolled in a study of a cholesterol-lowering drug is 230. If the guy whose true average cholesterol is 215 happens to spike at 235 on screening day, he’ll be enrolled. Meanwhile, the guy whose true average is 245 but happens to have a cholesterol level of 225 on screening day won’t be enrolled.
For the long answer, let’s show randomness in action with a larger group — say, 100 people. It would be easy to choose cholesterol numbers to demonstrate my point, but that would be cheating. So we’ll fire up Excel and put it to work.
First, we’ll create a subject pool. From what I’ve read, the average cholesterol level among adults living in industrial societies is around 220 – that is, if they’re not on statins. (The average is lower in many countries now because almost everyone with “high” cholesterol is given a prescription.) So we’ll create a pool of 100 potential subjects whose cholesterol levels more or less mimic the real-world population.
To do that, I told Excel to give 10 of the subjects a random cholesterol number between 140 and 300, the next 20 subjects a random number between 170 and 270, another 20 subjects a random number between 190 and 250, and the final 50 subjects a random number between 205 and 235.
The result was a wide range of cholesterol numbers with some outliers, but with the majority clustering within a stone’s throw of 220. I called those numbers the Real TC. Then to toss random chance into the mix, I told Excel to adjust each number by a random number between -30 and 30. I called those numbers the Screen TC. That’s the total-cholesterol number the researchers would record as the baseline for each patient.
As you can see, the averages of both the Real TC and the Screen TC (the one adjusted by random chance) are very close to each other, and also very close to the average among non-medicated adults. So to a researcher, these screening numbers would look about right.
But they’re not. Thanks to our random variations, some people whose true average cholesterol is above 230 will have a screening number below 230, and some people whose true average cholesterol is below 230 will have a screening number above 230. In theory, the effect would be the same in both directions. Therefore it’s no problem, right?
Wrong!!! It’s a huge problem. The true high-cholesterol subjects who score below 230 solely because of random variations are excluded from the study. In the data shown above, that would happen with Patient #97.
Meanwhile, the true low-cholesterol subjects who score above 230 solely because of random variations are included in the study. In the data above, that would be the case with Patient #99. The end result is that people whose true cholesterol is lower than their screened cholesterol are overrepresented in the patient pool. Once again, let’s let the numbers tell the story.
Remember, only people with a total cholesterol of 230 or above on screening day are enrolled in the study. So among those enrolled, there can be three effects of the random variation: someone with true high cholesterol can be screened as having even higher cholesterol (H > H), someone with true high cholesterol can be screened as having lower cholesterol that’s still at or above the cutoff of 230 (H > L), or someone with true low cholesterol can be screened as having high cholesterol (L > H).
After our random screening (courtesy of Excel), 35 of the 100 people screened ended up in the study. There were 11 subjects with true high cholesterol whose screening number was even higher, eight with true high cholesterol whose screening number was lower, and 16 with true low cholesterol (below 230), but who were screened at 230 or above — and thus ended up in the study group. That means out of our 35 study subjects, 27 have true cholesterol that’s lower than the number assigned on screening day. And again, this is all due to nothing more than random variations interacting with a cutoff number for enrollment.
Are you with me so far? Good.
Now let’s assume regression to the mean kicks in. At the end of the study, people whose cholesterol was spiking on screening day return to their true, lower average, which we’ll call Final TC. Likewise, people whose cholesterol was artificially low on screening day return to their true, higher average – but there are fewer of them for the reasons I explained above. Here’s what our average numbers look like.
Between screening day and the final day, our group showed better than an 11-point drop in cholesterol on average. Woo-hoo! The drug works!
Uh, but wait … I didn’t treat these people with any drug. The effect is totally due to random variations spread evenly across the potential-subject pool. Yes, the variation was equal in both directions. But when the variation was sufficiently downward, someone with true high cholesterol was excluded from the study. When the variation was sufficiently upward, someone with true low cholesterol was enrolled. When everyone regressed to the mean, the statistical effect was a drop in average cholesterol (at least as measured) among those enrolled.
Okay, let’s add one more twist. If the subjects enrolled were split evenly into a treatment group and a placebo group, then everything I described above should apply equally to both groups. We might see an artificial drop in cholesterol numbers across both groups, but no real difference between groups. So the researchers would have to report that the drug was no better than a placebo.
But as Chris Masterjohn pointed out in the article I linked in my previous post, that assumes we’re talking about studies with sufficiently large patient populations that are properly randomized. Let’s see what happens if we split our 35 enrollees into small groups.
To make sure I’m not cherry-picking, I took our study group and had Excel assign each subject a random number from 1 to 4. Then I divided the subjects by those numbers.
Just looking at groups 1 and 2 shows what can happen with small study groups. Suppose group 1 had been assigned the placebo and group 2 had been assigned the cholesterol-lowering drug. Look at the numbers. (Again, the Final TC here is just a return to each subject’s true average.)
Wow! The placebo group’s total cholesterol dropped just six points on average. But the treatment group’s total cholesterol dropped by an average of 19 points! Woo-hoo! The drug works!
But once again, I didn’t treat anyone. These numbers are all being produced by nothing more than random variations followed by a regression to the mean.
Now, if you’re a clever sort, you may already be typing your next comment … something along the lines of Oh, yeah, Mister Smarty Pants? Who says everyone would regress to the mean? If there are random variations on screening day, wouldn’t there also be random variations on the final testing day? Huh? HUH?!! And wouldn’t those cancel each other out?
Okay, let’s see for ourselves. Since our drug didn’t actually do anything (because we don’t actually have a drug), I took the true average cholesterol for each patient and, once again, told Excel to apply a random variation of between -30 and 30. That number became our new Final TC.
Adding random variation to the final measurement did, in fact, reduce the dramatic difference between groups 1 and 2.
Now it’s 12 points lower vs. 19 points lower. Not so impressive. But let’s suppose Group 3 had been our placebo group and Group 2 was still our treatment group.
Once again, we see a drop of just six points in the placebo group, but a drop of 19 points in the treatment group. Woo-hoo! If we make like a pharmaceutical marketer and express the difference in relative terms, our treatment was better than 200% more effective than the placebo!
But once again, there was no actual treatment effect whatsoever. The difference is entirely random. I didn’t have to throw out any outliers I don’t like or cherry-pick any data, either. I had Excel do all the random variations and group assignments for me to ensure that I couldn’t cherry-pick. All I did was choose a cutoff number for the study – total cholesterol of 230 or higher – and let randomness do the rest.
And yet, largely because of the small study size, I found an impressive difference between two of my groups – even after applying random variations to the final cholesterol measurement.
To repeat a quote from Chris Masterjohn:
And thus we see that many published research findings are false. Some of these false findings exist because we would inevitably expect by the laws of probability for a small handful of well conducted, thoroughly reported, and appropriately interpreted studies to uncover apparent truths that are really false simply by random chance. This emphasizes the need to look at the totality of the data. Some will be false because of regression to the mean. This emphasizes the need to critically evaluate the data in each study.
Makes you hope your doctor has an understanding of statistics and a desire to dig into the research before prescribing that wonder drug. But I wouldn’t bet on it.
Pop quiz: what’s the difference between the two x-y graphs below?
The answer is that they’re same graph — but the first picture above, we’ve zoomed in on one side of the bell curve.
In the book How Not To Be Wrong: A Guide To Mathematical Thinking, author Jordan Ellenberg points out that if you focus on a small part of a curve, it looks rather a lot like a line. Human like lines because they’re easy to understand. Plot some data and draw a line, and you have visual evidence that more of this produces more of that, or that more of this results in less of that. If the x-axis represents a span of time, you can even predict what the data will look like years from now by extending the line into the future.
The trouble is, real life tends to be more curved and less tidy than our beloved trendlines would have us believe. Two variables that were happily holding hands and running uphill together on our chart can suddenly break up and go their separate ways. So the trendline flattens out or reverses direction completely. That’s often how real life is, and if we don’t want to be wrong (as the title of the book says), we shouldn’t assume that a trendline heading in a particular direction will continue in that direction forever.
For example, you’ve probably seen headlines announcing that by the year 2040, everyone in America will be obese. Yup, every single person. Those predictions are based on charts that look something like this:
Researchers take a 25-year trendline that runs from 1990 to 2015 and simply extend it another 25 years. That is, of course, a wee bit ridiculous. Some decent-sized fraction of the population is highly resistant to becoming fat. Even if the Standard American Diet doesn’t undergo a positive shift (which I think it already is), the likely scenario is that when the actual data is plotted in 2040, it will look something like this:
Trendlines can flatten out. It happens all the time. It’s what we see when the law of diminishing returns kicks in. If I’ve been lifting weights once per month and switch to twice per month, I’ll likely become stronger. Kick it up to once per week and I’ll probably become stronger again. But at some point, more frequent workouts won’t make me stronger. There’s a limit to how quickly a body can produce new, stronger muscle cells.
Trendlines can also rise and then fall, producing a bell curve. More of this means more of that up to a point, but then even more of this leads to less of that. Interestingly (at least for those of us who enjoy reading about economics), Ellenberg introduces the concept by writing about taxes and the revenues they produce.
I’ve had debates with big-government-lovin’ acquaintances who are convinced there’s no national problem we couldn’t solve if we just had the political courage to seriously jack up taxes – especially on those darned rich people. In their minds, the relationship between tax rates and tax revenues looks like this:
Higher tax rates always produce higher revenues and therefore more government goodies for all – no matter how high the rates go.
But that’s not how it works in real life. Ellenberg doesn’t argue in favor of any particular tax rate, but he points out what any sane economist knows: at some point, higher tax rates produce less revenue, not more, because (among other reasons):
People have less disposable income to buy goods and services, which means fewer people will be employed to provide them.
More people decide to participate in the underground economy to avoid high taxes.
Fewer people can save the capital to start a new business.
People who already have the capital to start a new business decide they won’t bother if they’re taking all the risks but Uncle Sam is going to take most of the reward.
People in the highest income brackets often quit working for the year once any additional income they earn is taxed at a rate they find unacceptable.
So the relationship between tax rates and tax revenues is a bell curve:
Again, Ellenberg doesn’t advocate for a particular tax rate. He simply points out that in any given set of economic circumstances, there’s going to be a rate far greater than 0% and far less than 100% that produces the most revenue. If we only focus on the left side of the curve, we’ll end up believing that higher rates always produce more revenue. If we only focus on the right side of the curve, we’ll end up believing that lower rates always produce more revenue. Both beliefs are wrong – and of course, the title of the book is How Not To Be Wrong.
One way to avoid being wrong is to understand that in real life, the relationship between this-and-that often takes the form of a bell curve. Starting from a low level, more of something may be good … but that doesn’t necessarily mean a LOT more is better. Starting at a high level, less of something might also be good … but that doesn’t mean zero is better.
We see the bell-curve relationship all the time in the health sciences. How much vitamin D should you get? The answer would surely look something like this:
Starting from a low level, more is better. But that doesn’t necessarily mean a LOT more is better. At some point, a LOT more is toxic.
How much protein should you eat? We know too little is bad for your health. You’ll lose muscle mass and your immune system will weaken. But too much can cause diarrhea and dehydration. So the relationship between protein and health is a bell curve.
But my bell curve probably doesn’t peak at the same point yours does. And a muscular athlete who engages in heavy workouts would need protein than I do. So the relationship between protein and health may look something like this:
Not every relationship in health is a bell curve, of course. If we’re talking about rat poison and health, the relationship probably looks like this:
Less is better, period. Sometimes that’s the reality. Chareva’s mother is highly allergic to walnuts. So for her at least, the walnut-to-health relationship would look very much like the rat poison-to-health relationship above.
But getting back to the How Not To Be Wrong theme, here’s a graphic representation of a belief I once held, but now consider wrong:
Metabolic health is the highest at close to zero carbs, then goes down from there. I believed that because I went from a high-carb diet to a low-carb diet, and my health dramatically improved. So for awhile, I figured if low is good, close to zero must be even better. It may be true for some people, but it’s clearly not true for everyone.
On the low-carb cruise, I was pleased to hear Dr. Justin Marchiagiani respond to a question about ketogenic diets by saying he doesn’t recommend them for everyone. He works with each patient to find his or her ideal carbohydrate intake, which will depend on a number of factors. For some, it’s 50 grams per day or less; others feel better and lose weight more easily at 100 grams per day or more. He said he feels at his best at between 75 and 100 grams per day. That’s about where I am now as well.
There is no level of carb intake that’s ideal for everyone. So the relationship between carbs and metabolic health looks something like this:
If we only focus on the right side of the curve, we’ll end up believing that more carbs always means worse metabolic health, so the ideal level of carb intake is close to zero.
But that ignores the left side of the curve. And that’s an easy way to be wrong.
The film follows Donal – a lean, fit, seemingly healthy 41 year old man – on a quest to hack his genes and drop dead healthy by avoiding the heart disease and diabetes that has afflicted his family.
Donal’s father Kevin, an Irish gaelic football star from the 1960s, won the first of 2 All Ireland Championships with the Down Senior Football Team in 1960 before the biggest crowd (94,000) ever seen at an Irish sporting event.
When Kevin suffered a heart attack later in life, family and friends were shocked. How does a lean, fit and seemingly healthy man – who has sailed through cardiac stress tests – suddenly fall victim to heart disease?
Can a controversial diet consisting of 70% fat provide the answers?