This study found that women who win a Best Actress Oscar have a much higher rate of divorce in the following years than the losing Best Actress nominees and the Best Actor nominees, both winning and losing. A Chinese joke I heard recently says essentially the same thing:
There are four kinds of people: 1. Man. 2. Woman. 3. Woman with a Ph.D. 4. Someone who will marry a woman with a Ph.D.
Via Marginal Revolution.
… is the charming title of this post by Adam Stoffa. Actually, reading me keeps him asleep. Adam read my long self-experimentation paper and came across my discovery that skipping breakfast reduced early awakening. He had early awakening:
I would wake up sometime between 0400 and 0430. Six hours of sleep was not good. This was a problem that needed my attention.
Like me, he was eating breakfast around 7 am and waking up three hours earlier. He ate a big breakfast. He decided to make breakfast later rather than skip it.
I started experimenting with a late breakfast in August. I was traveling through multiple time zones at the time. So I had no idea whether it was working. But by the time I got back to Korea, eating a late breakfast was becoming a habit.
After recovering from my jet lag, I noticed that the experiment was working. Now, I wake up and get out of bed between 0600 and 0615. Sometimes, I still wake up in the middle of the night, but after a quick bathroom break, I’m right back to sleep. I get to work at 0800. After working for an hour, I take a break and eat (e.g. 3 hard boiled eggs, +/- a cup of cashews or macadamia nuts, and a smoothie). This regiment has been working well for eight weeks and shows no signs of weakening.
Yay!
I’ve said it before but it is worth repeating: Science and job don’t mix very well. Career demands can make it hard to tell the truth as you see it. A scientist named Norman Rogers put it like this in relation to global warming:
Mainstream climate scientists are asking for trouble if they become skeptics [about man-made global warming]. They may lose their jobs, their papers may not be published and they may lose their grants. Thatʼs why most skeptics are older or retired or from outside of the mainstream – they are less vulnerable to retaliation.
He could have added that global-warming skeptics will have difficulty recruiting others, such as graduate students, to work with them and will face disdain from their colleagues.
I saw this in relation to the work of Ranjit Chandra. At Berkeley, when I told other professors about my doubts, one of them replied: Talk to X. He’s had doubts about Chandra for 30 years. I spoke to X. This was correct. I didn’t ask X why he’d never said anything publicly about it because the reason was obvious: He feared retaliation.
In August, at a Quantified Self meeting in San Jose, I told how butter apparently improved my brain function. When I started eating a half-stick of butter every day, I suddenly got faster at arithmetic. During the question period, Greg Biggers of genomera.com proposed a study to see if what I’d found was true for other people.
Eri Gentry, also of genomera.com, organized an experiment to measure the effect of butter and coconut oil on arithmetic speed. Forty-five people signed up. The experiment lasted three weeks (October 23-November 12). On each day of the experiment, the participants took an online arithmetic test that resembled mine.
The participants were randomly assigned to one of three groups: Butter, Coconut Oil, or Neither. The three weeks were divided into three one-week phases. During Phase 1 (baseline), the participants ate normally. During Phase 2 (treatment), the Butter participants added 4 tablespoons of butter (half a stick of butter) each day to their usual diet. The Coconut-Oil participants added 4 tablespoons of coconut oil each day to their usual diet. The Neither participants continued to eat normally. During Phase 3 (baseline), all participants ate normally.
After the experiment was finished. Eri reduced the data set to participants who had done at least 10 days of testing. Then she made the data available. I wanted to compute difference scores (Phase 2 MINUS average of Phases 1 and 3) so I eliminated someone who had no Phase 3 data. I also eliminated four days where the treatment was wrong (e.g., in the sequence N N N N N B B N N B, where N = Neither and B = Butter, I eliminated the final Butter day). That left 27 participants and a total of 443 days of data.
Because the scores on individual problems were close to symmetric on a log scale, I worked with log solution times. I computed a mean for each day for each participant and then a mean for each phase for each participant.
This figure shows the means for each phase and group. The downward slopes show the effect of practice. The separation between the lines shows that individual differences are large. (There was no reliable difference between the three groups during Phase 1.)
The point of the baseline/treatment/baseline design is allow for a large practice effect and large individual differences. It allows a treatment effect to be computed for each participant by computing a difference score: Phase 2 MINUS average of Phases 1 and 3. The average of Phases 1 and 3 estimates what the results would be if the treatment made no difference.
This graph shows the difference scores. There are clear differences by group. A Wilcoxon test comparing the Butter and Neither groups gives one-tailed p = 0.006.
The results support my idea that butter improves brain function. They also suggest that coconut oil does not. In the next post I’ll discuss what else I learned from this experiment.
Irritable bowel syndrome (IBS) — basically, recurrent pain during digestion — is common. A new study by Italian pediatricians asked if a probiotic would help. They randomized children into two groups: active and placebo. Children in the active group were given pills with a lot of lactobacillus bacteria, which they took twice per day. The placebo was made by the same manufacturer, so it looked identical. During the study, the researchers did not know who was in each group.
There was a big difference between the groups, which took about four weeks to emerge. The active group had painful episodes less than half as often as the placebo group, and the episodes they did have were less painful.
Overall this supports my broad point that we need to eat plenty of fermented foods to be healthy. That’s not what the authors of the study concluded. They concluded:
Demonstration of the efficacy of a given probiotic for a specific therapeutic target will help clinicians choose which probiotic to use when dealing with a specific disease. We are entering the era of targeted probiotic use.
Which reveals a bad case of gatekeeper syndrome. I wouldn’t expect them to say their results support the idea that everyone should eat fermented foods — that’s an “alternative” (and therefore “crazy”) idea. But they could have said their results imply that kids with IBS should eat yogurt.
My friends know I like examples. My mother has complained I like them too much. Here, via Jonathan Schwarz, is a good example of why I like examples. From a long article by Bill Keller, executive editor of the New York Times:
I’m the first to admit that news organizations, including this one, sometimes get things wrong. We can be overly credulous (as in some of the prewar reporting about Iraq’s supposed weapons of mass destruction) or overly cynical about official claims and motives.
Emphasis added. The lack of an example of being “overly cynical about official claims and motives” speaks volumes about the New York Times’ relationship to those in power.
The New York Times reported a study in Science that found that testing yourself on material you have learned is a good way to improve retention. Then they published a set of letters about it.
John Taylor Gatto, whose books I like, wrote:
Real learning is measured only by utility, by application. In the case of this research, the success claimed for practice testing is being measured by further testing — not by any real-world application.So what? Nobody should care whether memorization is enhanced by practice testing or not.
I disagree. Every day I study Chinese. A lot of that study is memorization, such as what characters mean. Learning what the characters mean while studying in my apartment really does help me understand what they mean out in the world. I care a lot how to memorize better.
A Pace University professor and “director of learning assessment” wrote:
Studying, not test taking, is the key to learning. . . .Testing, particularly standardized testing, does nothing to enhance knowledge and hinders the development of an appreciation for learning that should begin in school and last a lifetime.
I couldn’t disagree more. After I have studied Chinese, frequently testing myself on what I’ve learned turns out to be essential to long-term retention. Without those tests — say, daily for a week, and less often after that — I forget what I’ve learned.
Standardized testing is especially helpful because it helps me see what works and what doesn’t work. It makes it easier to compare various conditions, in other words.
“Development of an appreciation for learning that should begin in school . . . ” Should? I enjoyed learning long before I started school.
It has taken me a few years to figure out how to learn Chinese. Now I think I am on the right track but these letters illustrate what my self-experimentation also taught me: Experts say the darndest things.
The first Quantified Self conference will be held in San Jose May 28-29! Robin Barooah and I plan to lead a break-out session about self-experimentation.
After referring to Jane Jacobs (“successful city neighborhoods need a mixture of old and new buildings”), which I liked, Tim Harford wrote this, which I didn’t like:
Many medical treatments (and a few social policies) have been tested by randomized trials. It is hard to imagine a more clear-cut practice of denying treatment to some and giving it to others. Yet such lotteries — proper lotteries, too — are the foundation of much medical progress.
The notion of evidence-based medicine was a step forward in that it recognized that evidence mattered. It was only a small step forward, however, because its valuation of evidence — on a single dimension, with double-blind randomized trials at the top — was naive. Different sorts of decisions need different sorts of evidence, just as Jacobs said different sorts of businesses need different sorts of buildings. In particular, new ideas need cheap tests, just as new businesses need cheap rent. As an idea becomes more plausible, it makes sense to test it in more expensive ways. That is one reason a monoculture of evidence is a poor idea.
Another is that you should learn from the past. Sometimes a placebo effect is plausible; sometimes it isn’t. To ignore this and insist everything should be placebo-controlled is to fail to learn a lot you could have learned.
A third reason a monoculture of evidence is a poor idea is that it ignores mechanistic understandings — understanding of what causes this or that problem. In some cases, you may think that the disorder you are studying has a single cause (e.g., scurvy). In other cases, you may think the problem probably has several causes (e.g., depression, often divided into endogenous and exogenous). In the latter case, it is plausible that a treatment will help only some of those with the problem. So you should design your study and analyze your data taking into account that possibility. You may want to decide for each subject whether or not the treatment helped rather than lump all subjects together. And the “best” designs will be those that best allow you to do this.
I love this show! It is to comedy what Mad Men is to drama.