# I have whatever the opposite of a placebo effect is

Two personal stories:

When I first started working a full-time job, I started tracking my daily (subjective) productivity along with a number of variables that I thought might be relevant, like whether I exercised that morning or whether I took caffeine. I couldn’t perceive any differences in productivity based on any of the variables.

After collecting about a year of data, I ran a regression. I found that most variables had no noticeable effect, but caffeine had a huge effect—it increased my subjective productivity by about 20 percentage points, or an extra ~1.5 productive hours per day. Somehow I never noticed this enormous effect. Whatever the opposite of a placebo effect is, that’s what I had: caffeine had a large effect, but I thought it had no effect.

People always say that exercise helps them sleep better. I thought it didn’t work for me. When I do cardio, even like two hours of cardio, I don’t feel more tired in the evening and I don’t fall asleep (noticeably) faster.

Yesterday, I decided to test this. I wrote a script to predict how long I slept based on how many calories my phone says I burned. The idea is that if I sleep less, that probably means I didn’t need as much because my sleep was higher quality. (I almost always wake up naturally without an alarm.)

Well, turns out exercise does help. For every 500 calories burned (which is about what I burn during a normal cardio session), I sleep 25 minutes less. Once again, exercise had a huge effect, and I thought it didn’t do anything.

I guess I’m not very observant.

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# Protein Quality Calculator

Last updated 2024-09-06 to add more content.

You may know that complete proteins are good because they contain every essential amino acid. But you might not know that that’s not the full story.

Take wheat. Wheat is a complete protein—it contains all nine essential amino acids. But it has a problem. Wheat only contains 27mg of lysine (an essential amino acid) per gram of protein, whereas the Food and Agriculture Organization recommends 48mg of lysine per gram. To make full use of a gram of protein, your body needs to get those 48mg. It doesn’t matter that wheat has lots of other essential amino acids. Once your body uses up all the lysine, it can’t make good use of the other amino acids in wheat protein.

You can evaluate the protein quality of a food using the Digestible Indispensable Amino Acid Score (DIAAS). This score determines the quality of a source of protein based on which essential amino acid will run out first, adjusted for digestibility. A score of 100 means the protein has plenty of every essential amino acid.

Sometimes you can improve the protein quality of your food by mixing different ingredients. Wheat has a DIAAS of 57 because it only has 57% as much lysine per gram as your body needs. Peas have a score of 82 because they don’t have enough methionine + cysteine. But peas have 131% of the lysine requirement, and wheat has 149% of methionine + cysteine, so mix them together and they cover for each other’s weaknesses. A 50/50 mixture of wheat and pea protein has a DIAAS of 94.

With this calculator, you can determine the DIAAS for mixtures of different protein sources.

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# Just because a number is a rounding error doesn't mean it's not important

Sometimes, people call a number a “rounding error” as if to say it doesn’t matter. But a rounding error can still be very important!

Say I’m tracking my weight. If I’ve put on 0.1 pounds since yesterday, that’s a rounding error—my weight fluctuates by 3 pounds on a day-to-day basis, so 0.1 pounds means nothing. But if I continue gaining 0.1 pounds per day, I’ll be obese after 18 months, and by the time I’m 70 I’ll be the fattest person who ever lived.

Or if the stock market moves 1% in a day, that’s a rounding error. If it moves up 1% every day for a year, every individual day of which is a rounding error, it will be up 3700%, which would be the craziest thing that’s ever happened in the history of the global economy.

This happens whenever the standard deviation is much larger than the mean. A large standard deviation means a “real” change gets obscured by random movement. But over enough iterations, the random movements even out and the real changes persist. For example, the stock market has an average daily return of 0.02% and a standard deviation of 0.8%. The standard deviation is 40x larger than the mean, so a real trend in prices gets totally washed out by noise. The market’s daily average return is a rounding error, but it’s still important.

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# A 401(k) Sometimes Isn't Worth It

You don’t always save money by putting your investments into a 401(k).

When you invest money inside a 401(k), you don’t have to pay taxes on any returns earned by your investments. But you also have to pay a fee to your 401(k) provider.

• If you buy and hold index funds in a taxable account, you don’t have to pay any capital gains tax on price increases until you sell.
• In a 401(k), the annual fee adds up every year and may eventually exceed the tax savings.

So the taxes cap out at the capital gains tax rate (15% or 20% depending on your tax bracket),1 whereas the expenses of a 401(k) continue to accumulate.

However, in a taxable account, you do still have to pay taxes on dividends (and bond payouts) every year, and those taxes might cost you more than the 401(k) fees.2

Below is a calculator to determine how many years before the 401(k) fees exceed the tax savings, if ever.

 employer matching (%) total investment return including dividends (nominal) (%) dividend yield (%) 401(k) fee (%) capital gains tax rate (%) income tax rate today (%) income tax rate in retirement (%)

 A 401(k) falls behind a taxable account after:

This calculator assumes you buy index funds and hold them forever. If you trade stocks within a taxable account, you have to pay taxes every time you make a trade.

Something else to consider: If you quit your job, your old employer’s 401(k) provider will let you roll your 401(k) into an IRA. You don’t have to pay any fees on an IRA.3 So even if the 401(k) fees exceed the tax benefits after (say) 30 years, that’s not a problem if you expect to quit your job after less than 30 years. Realistically, few people stay at one job for so long that the 401(k) fees exceed the tax savings.

(If you change jobs, usually you can roll your old 401(k) into your new 401(k), but I wouldn’t do that because it means you have to keep paying 401(k) fees. It’s almost always better to roll your old 401(k) into an IRA.)

## Notes

1. The capital gains tax will always be less than 15%/20% of your account value (depending on which tax bracket you’re in), but it converges on 15%/20% as the value approaches infinity.

Example: If you invest $100 in an index fund and you sell when the price reaches$101, you have to pay 20% of $1 (assuming you’re in the 20% tax bracket), which is only 0.2% of the total value. If you sell when the price reaches$1 million, you have to pay 20% of \$999,900, which is 19.998% of the total value.

2. H/T Ben Kuhn for raising this possibility. I’m sure someone somewhere had considered it before him, but I’ve never seen anyone else bring it up, and standard financial advice ignores it.

3. Other than ETF/mutual fund fees, but you have to pay those no matter what.

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# Continuing My Caffeine Self-Experiment

I did another caffeine experiment on myself. This time I tested if I could have caffeine 4 days a week without getting habituated.

Last time, when I took caffeine 3 days a week, I didn’t get habituated but the results were weird. This time, with the more frequent dose, I still didn’t get habituated, and the results were weird again!

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# Some Things I've Changed My Mind On

Here are some things I’ve changed my mind about. Most of the changes are recent (because I can remember recent stuff more easily) but some of them happened 5+ years ago.

I’m a little nervous about writing this because a few of my old beliefs were really dumb. But I don’t think it would be fair to include only my smart beliefs.

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# What's the Healthiest Body Composition?

Last time, I found that the healthiest BMI range for all-cause mortality is 20–22. But BMI doesn’t tell the whole story. Most obviously, it doesn’t account for body fat vs. lean mass. All else equal, you’d rather have more muscle1 and less fat.

So what’s the healthiest combination of lean mass + fat mass?

I’m not going to answer that question because I can’t. Instead, I will explain why I can’t, and then give a rough guess at the answer.

Scientists have been measuring and collecting data on BMI for decades. You can find plenty of giant BMI studies with three million participants in various countries.

We have much sparser data on body fat. Scientists didn’t start collecting data on body fat until the last few decades. And body fat is harder to measure—we have various methods for estimating body fat, but they’re all more complicated than calculating BMI.

I managed to scrounge together some studies on body fat and mortality. My best guess: the average woman should aim for a BMI of 21 with 20% body fat, and the average man a BMI of 21 with 10% body fat. (Subject to individual variation due to genetics and whatnot.)

Trans men should probably target the same body fat % as cis men, and likewise for trans women and cis women, because hormone therapy alters body fat distribution (Spanos et al. (2020)2).

The evidence weakly suggests that there is no lower bound on healthy fat mass, and no upper bound on healthy lean mass. We have so little mortality data on extremely lean + muscular people that we can’t say how healthy they are.

A more in-depth analysis would look at a variety of health indicators (blood pressure, HDL cholesterol, etc.) and use that to predict mortality. I didn’t do that, I just looked at mortality data.

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TLDR: 20 to 22.

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# Caffeine Cycling Self-Experiment

Last updated 2024-07-26 to clarify wording.

Confidence: Likely.

I conducted an experiment on myself to see if I would develop a tolerance to caffeine from taking it three days a week. The results suggest that I didn’t. Caffeine had just as big an effect at the end of my four-week trial as it did at the beginning.

This outcome is statistically significant (p = 0.016), but the data show a weird pattern: caffeine’s effectiveness went up over time instead of staying flat. I don’t know how to explain that, which makes me suspicious of the experiment’s findings.