A Chinese university press has printed course materials from Yale’s free online classes. Yale has complained, okay, but the incident reminds me of something Stan Ulam attributed to John von Neumann: “It’s amazing what you can accomplish if you are willing to give someone else the credit.”
Via Andrew Gelman.
Thanks to Grace Jone, Anne Weiss and Bryon Castañeda.
A new study in JAMA found higher salt consumption strongly associated with less death from heart disease. The association with total mortality (more salt, less death) was almost significant. To grasp the strength of the evidence, see this. Yes, it’s a correlation, but I don’t know of any examples of such a strong correlation reversing (so that more salt is now correlated with more death) when now-unknown confounders are taken into account. In 1998, Gary Taubes argued that the benefits of salt reduction were greatly overstated. The new study did find more salt correlated with higher systolic blood pressure but in the big picture (mortality) that didn’t matter. If all those warnings about salt had any effect, the new study suggests their effect was negative.
Perhaps people who eat less salt are more credulous (they believed the experts) — and this damages them in other ways? Perhaps they rely on doctors more, for example. It is hard to interpret this finding in a way that makes mainstream health care look good. A New York Times article about the study points out that “the new study is not the only one to find adverse effects of low-sodium diets.” And it reports what someone at the Centers for Disease Control said:
Dr. Peter Briss, a medical director at the centers, said that the study was small; that its subjects were relatively young, with an average age of 40 at the start; and that with few cardiovascular events, it was hard to draw conclusions.
Dr. Briss fails to understand statistics. Ordinary statistical calculations take sample size and number of events into consideration when indicating the strength of the evidence. That’s the one of the main purposes of those calculations. As for “relatively young,” I know of nothing to suggest that the effects of sodium reverse with age — so it is irrelevant that the subjects were relatively young. That someone at the CDC is so clueless is remarkable.
This is a great documentary about a new cancer treatment invented by a Texas doctor named Stanislaw Burzynski. You can watch it for free until June 20 (and perhaps later, since an earlier deadline was extended).
This shows Justin Wehr‘s weight over two years. He is 26 years old and 6 feet 2 inches tall — at 140 pounds, very thin. The record begins with a switch to a vegan diet. Over three months he lost seven pounds but gradually regained the lost weight, even though his diet didn’t change. In the middle he suddenly gained five pounds on a trip to Alaska and Seattle and then gradually lost it.
He describes his diet like this:
My diet was pretty average Midwestern meat and potatoes sort of thing prior to going vegan-ish, and I emphasize the ish. I’ve been vegan-ish since I started tracking weight, meaning that I don’t buy meat or dairy products at the store, but I’ll happily eat whatever sounds good off of a restaurant menu or whatever is being served when I’m eating at someone else’s place. I intentionally keep my diet very boring. I eat an absurd amount of peanuts and raisins, I estimate in the range of 600 – 900 calories per day. Besides peanuts and raisins, most of my calories come from lentils, frozen vegetables, and bread + olive oil. I drink almost exclusively water, with a few swigs of OJ most days, and have a glass of wine or a bottle of beer on occasion.
The features of this data that interest me are (a) weight loss when he changed what he ate (first three months) and (b) gradual regain of the lost weight (after that). Few theories of weight control can explain the regain. However, the theory behind the Shangri-La Diet can. It says that he initially lost weight because he shifted to foods with relatively weak flavor-calorie associations — weak because the foods were relatively new. As he ate them again and again, the flavor-calorie associations got stronger and this raised his set point.
When The Shangri-La Diet was published (2006), I enjoyed checking its Amazon rank. The rank got worse. I checked less often. Eventually it was usually above 100,000 and I barely checked at all.
A few months ago, I noticed it was much better than I expected — maybe 40,000. How did that happen? Were sales improving? To find out, I subscribed to RankTracer, which records Amazon rank every hour and plots the results.
Here are the first two months of data from RankTracer:
This resembles the graphs that RankTracer makes. Whether the rank is steadily improving isn’t clear. Here is the same data with a logarithmic y axis:
Now steady improvement is obvious.
I’m pretty sure that slowly increasing sales five years after publication is extremely rare. But a bizarre sales record is entirely consistent with two recent comments on the SLD forums. One is this:
It does work, and it is totally boggling that something so counter-intuitive would work. . . . You don’t have to devote your life to starving and working out. One of the best-kept secrets of all time.
The other is this:
I refuse to get drawn into ‘how crazy’ it sounds … I just like the results.
A 2010 study (via Whole Health Source) compared a Paleo diet with a Mediterranean diet. For twelve weeks, twenty-nine volunteers could eat as much as they wanted, whenever they wanted. Half ate Paleo (“lean meat, fish, fruit, vegetables, root vegetables, eggs, and nuts”), half Mediterranean (“whole grains, low-fat dairy products, vegetables, fruit, fish, and oils and margarines”).
The main result was that the Paleo food was “more satiating per calorie”. The Paleo eaters ate less but no more often than Mediterranean eaters. The paper does not report weight loss. (See an earlier report of the same experiment for that.)
I suspect the Paleo diet was less familiar than the Mediterranean diet (of course I can’t be sure from the descriptions). My theory of weight control says familiarity matters: less-familiar food pushes your set point lower than familiar food because its smell-calorie associations are weaker. The smells of less-familiar food is less associated with calories than the smells of more familiar food. With a lower set point, you will need less food to feel full.
If familiarity matters, this causes big problems for clinical studies. It means that short-term results (e.g., after 6 months) may be quite different than long-term results (e.g., after 2 years) — and most clinical trials last about six months. Short term, says my theory, any new food will cause weight loss. Indeed, a wide range of diets that cause dieters to eat new foods, such as the cabbage soup diet, cause short-term weight loss. Over the long term, however, the new foods become familiar and, according to my theory, the set point goes back up as the new smell-calorie associations are learned. Indeed, on most diets there is great long-term weight regain. If familiarity matters, we need data sets lasting a long time (e.g., nine or ten years) to understand weight control. Such data sets allow enough time for the chosen diets to become familiar so that (a) the diets being compared are equal in familiarity and (b) we can see their long-term effects — which may easily be different from their short-term effects.
Thanks to Steve Hansen.
Kefir is much easier to make than yogurt because you ferment the milk at room temperature, once you have the starter culture. I’ve made it about ten times. The most recent batch was the easiest and best because I learned two things from the woman who gave me the starter:
1. Ferment it until there is a line of separation. There eventually form a line of clear liquid between the curds (top) and the rest (bottom). This took about two days. In the past I didn’t know how long to wait.
2. After fermentation, separate the curds from the rest by putting it through a colander. This provides good separation. You drink the liquid, use the solids to make more kefir. In the past I tried to spoon out the kefir grains.
If I had to choose between kefir and yogurt I’d choose kefir. Not only is it easier to make but it is far more complex. Unlike yogurt, it’s a drink. I drink more often than I eat so there are more opportunities to consume it.
I suspect you can make kefir by putting store-bought kefir into ordinary milk. I haven’t tried this, however.
Conway’s Law is the observation that the structure of a product will reflect the structure of the organization that designed it. If the organization has three parts, so will the product. In the original paper (1968), Conway put it like this:
Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure.
Here is an example:
A contract research organization had eight people who were to produce a COBOL and an ALGOL compiler. After some initial estimates of difficulty and time, five people were assigned to the COBOL job and three to the ALGOL job. The resulting COBOL compiler ran in five phases, the ALG0L compiler ran in three.
A consumer — someone outside the organization who uses the product — wants the best design. Conway’s Law implies they are unlikely to get it.
I generalize Conway’s Law like this: It is hard for people with jobs to innovate — for reasons that outsiders know nothing about. Whereas persons without jobs have total freedom. An example is a politician who promises change but fails to deliver. The promises of change are plausible to outsiders (voters) so they elect the politician. However, being outsiders, they barely understand how government works. When the promised changes don’t happen, the voters are “disillusioned”.
To me, the most interesting application of the generalized law is to science. In my experience, people who complain about “bad science”, such as John Ioannides and Ben Goldacre, have the same incomplete view of the world as the “disillusioned” voters. They fail to grasp the constraints involved. They fail to consider that the science they are criticizing may be the best those professional scientists can produce, given the system within which they work. Better critiques would look at the constraints the professional scientists are under, the reasons for those constraints, and how those constraints might be overcome.
“Much research is conducted for reasons other than the pursuit of truth,” writes Ioannidis. Well, yes — people with jobs want to keep them and get promoted. They want to appear high status. That’s not going to change. It’s absolutely true that drug company scientists slant the evidence to favor their company’s drug, as Irving Kirsch explains in The Emperor’s New Drugs. But if you don’t understand what causes depression and you’re trying to produce a new anti-depressant and you want to keep your job . . . things get difficult. The core problem is lack of understanding. Lack of understanding makes innovation difficult. Completely failing to understand this, Ioannidis recommends something that would discourage new ideas: “We must routinely demand robust and extensive external validation—in the form of additional studies—for any report that claims to have found something new.”
Truly “bad science” has little to do with what Ioannides or Goldacre or any quackbuster talks about. Truly bad science is derivative science, science that fails to find new answers to major questions, such as the cause of obesity. Failure of innovation isn’t shown by any one study. Given the rarity of innovation, it is unwise to expect much of any one study. To see lack of innovation clearly you need to look at the whole distribution of innovation. Whether the system is working well or poorly, I think the distribution of innovation resembles a power law: most studies produce little progress, a tiny number produce large progress. The slope of the distribution is what matters. Bad science = steep downward slope. With bad science, even the most fruitful studies produce only small amounts of innovation.
Just as outsiders expect too much from professionals, they fail to grasp the innovative power of non-professionals. Mendel was not a professional scientist. Darwin was not a professional scientist. Einstein did his best work while a patent clerk. John Snow, the first person to use data (a graph) to learn the cause of an infection, was a doctor. His job had nothing to do with preventing infection. To improve innovation about health (or anything else), we should give more power to non-professionals, as I argued in my talk at the First Quantified Self Conference.
Thanks to Robin Barooah.
