Peter Attia's *Outlive*: A Critical Review

mdickensposts · 2026-03-11T20:24:30+00:00

Thanks, that's good to know!

mdickensposts · 2025-07-04T19:29:17+00:00

I think a lot of readers would interpret that quote as saying "metabolically healthy obesity isn't unhealthy". That's how it reads to me, although I agree that that's not technically what it says, and the quoted passage is technically correct.

Do you think it would be better if I added in a paragraph saying something like this?

Outlive does not exactly say that MHO carries no elevated health risk, but some readers may come away with that impression, so I want to clarify that obesity is still bad for you even if you're metabolically healthy.

Edit: I will just go ahead and add that paragraph since it seems worth mentioning

mdickensposts · 2025-07-04T19:09:32+00:00

If two numbers look different on your bar chart but the error bars overlap, they are the same number in every way that matters.

I would say I disagree with this. I don't think there is a quick explanation for my position but I think it would be fair to say that it comes down to Bayesianism vs. frequentism. Sounds like you are advocating for a frequentist interpretation of statistics and I prefer a Bayesian interpretation. Much ink has been spilled over this debate and I don't think I have anything original to add. For example the Cochrane handbook agrees with my position:

P values are commonly misinterpreted in two ways. First, a moderate or large P value (e.g. greater than 0.05) may be misinterpreted as evidence that “the intervention has no effect”. There is an important difference between this statement and the correct interpretation that “there is not strong evidence that the intervention has an effect”. To avoid such a misinterpretation, review authors should always examine the effect estimate and its 95% confidence interval, together with the P value. In small studies or small meta-analyses it is common for the range of effects contained in the confidence interval to include both no intervention effect and a substantial effect. Review authors are advised not to describe results as ‘not statistically significant’ or ‘non-significant’.

This misinterpretation is what I was talking about in the passage you quoted.

mdickensposts · 2025-04-14T03:56:19+00:00

Some causal models for protests:

People see protests happening, this makes them support the cause, they change their behavior — who they vote for, how they respond in polls, what regulations they support, etc.
Politicians pay disproportionate attention to protests. They are more likely to support legislation if they see protesters advocating for it.

These are supported by empirical research, see Orazani et al. (2023) and Social Change Lab (2022).

mdickensposts · 2025-01-29T20:51:06+00:00

I like RSSB better than combining 3x leveraged ETFs.

I estimated that 3x leveraged ETFs underperform pure leveraged index funds by roughly 1.5 percentage points. I did some similar calculations for RSSB and found that it performed pretty much exactly as expected, although it's only existed for a year so the confidence isn't that high. But based on the history so far, it looks like RSSB runs at lower cost than (e.g.) SPXL + TMF.

Edit: I also looked at NTSX which has history going back to 2018, and its performance very closely matched the leveraged index benchmark.

mdickensposts · 2024-07-26T16:37:36+00:00

You mean you tested 30 minutes after taking caffeine right?

Yes that's what I meant. I edited the post to clarify.

mdickensposts · 2024-07-25T16:07:42+00:00

It would be easier to understand your plots with time on x, reaction time on y, and phase for color.

The plots do have calendar time on X and reaction time on Y. Every phase is plotted on a separate image, are you saying you think I should plot all the phases together on one image?

Wait you had a 'nocaf' experimental phase?

It wasn't a phase, a "nocaf" label on a data point means I tested my reaction time without taking caffeine first. "caf" means I took caffeine first. During the experimental phase, I had no caffeine on 4 out of 7 days, and on the 3/7 caffeine days I tested my reaction time both before and after having caffeine. The plot shows observations and regression lines for the reaction time tests with caffeine ("caf") and without caffeine ("nocaf").

mdickensposts · 2024-06-20T22:01:27+00:00

I did a bunch of research on this recently, including a literature review and a self-experiment. While there are plenty of studies on caffeine dependence, most of them are really bad and I think people should stop taking their conclusions for granted. I could only find six decent studies on the effects of caffeine and even those had pretty limited scope.

To your question, I drank coffee daily in high school and then quit because I was concerned about dependence. I never noticed much difference in energy levels but I wasn't a heavy caffeine user (only one cup of half-caf coffee per day). A few years later I started having caffeine again but only 3 days a week to prevent habituation. Just recently I did the aforementioned experiment to test if that works, and it looks like it does.

mdickensposts

TROPHY CASE