My view is constantly popping back and forth between different views

That sounds like you engage in binary thinking and don't value shades of grey of uncertainty enough. You feel to need to judge arguments for whether they are true or aren't and don't have mental categories for "might be true, or might not be true".

Jonah makes strong claims for which he doesn't provide evidence. He's clear about the fact that he hasn't provided the necessary evidence.

Given that you pattern match to "crackpot" instead of putting Jonah in the mental category where you don't know whether what Jonah says is right or wrong. If you start to put a lot of claims into the "I don't know"-pile you don't constantly pop between belief and non-belief. Popping back and forth means that the size of your updates when presented new evidence are too large.

Being able to say "I don't know" is part of genuine skepticism.

I'm sympathetic to everything you say.

In my experience there's an issue of Less Wrongers being unusually emotionally damaged (e.g. relative to academics) and this gives rise to a lot of problems in the community. But I don't think that the emotional damage primarily comes from the weird stuff that you see on Less Wrong. What one sees is them having born the brunt of the phenomenon that I described here disproportionately relative to other smart people, often because they're unusually creative and have been marginalized by conformist norms

Quite frankly, I find the norms in academia very creepy: I've seen a lot of people develop serious mental health problems in connection with their experiences in academia. It's hard to see it from the inside: I was disturbed by what I saw, but I didn't realize that math academia is actually functioning as a cult, based on retrospective impressions, and in fact by implicit consensus of the best mathematicians of the world (I can give references if you'd like) .

Thanks so much for sharing. I'm astonished by how much more fruitful my relationships have became since I've started asking.

I think that a lot of what you're seeing is a cultural clash: different communities have different blindspots and norms for communication, and a lot of times the combination of (i) blindspots of the communities that one is familiar with and (ii) respects in which a new community actually is unsound can give one the impression "these people are beyond the pale!" when the actual situation is that they're no less rational than members of one's own communities.

I had a very similar experience to your own coming from academia, and wrote a post titled The Importance of Self-Doubt in which I raised the concern that Less Wrong was functioning as a cult. But since then I've realized that a lot of the apparently weird beliefs on LWers are in fact also believed by very credible people: for example, Bill Gates recently expressed serious concern about AI risk.

If you're new to the community, you're probably unfamiliar with my own credentials which should reassure you somewhat:

• I did a PhD in pure math under the direction of Nathan Dunfield, who coauthored papers with Bill Thurston, who formulated the geometrization conjecture which Perelman proved and in doing so won one of the Clay Millennium Problems.

• I've been deeply involved with math education for highly gifted children for many years. I worked with the person who won the American Math Society prize for best undergraduate research when he was 12.

• I worked at GiveWell, which partners with with Good Ventures, Dustin Moskovitz's foundation.

• I've done fullstack web development, making an asynchronous clone of StackOverflow (link).

• I've done machine learning, rediscovering logistic regression, collaborative filtering, hierarchical modeling, the use of principal component analysis to deal with multicollinearity, and cross validation. (I found the expositions so poor that it was faster for me to work things out on my own than to learn from them, though I eventually learned the official versions).You can read some details of things that I found here. I did a project implementing Bayesian adjustment of Yelp restaurant star ratings using their public dataset here

So I imagine that I'm credible by your standards. There are other people involved in the community who you might find even more credible. For example: (a) Paul Christiano who was an international math olympiad medalist, wrote a 50 page paper on quantum computational complexity with Scott Aaronson as an undergraduate at MIT, and is a theoretical CS grad student at Berkeley. (b) Jacob Steinhardt, a Hertz graduate fellow who does machine learning research under Percy Liang at Stanford.

So you're not actually in some sort of twilight zone. I share some of your concerns with the community, but the groupthink here is no stronger than the groupthink present in academia. I'd be happy to share my impressions of the relative soundness of the various LW community practices and beliefs.

I feel that I need to maintain extreme mental efforts to stay "sane" here. Maybe I should refrain from commenting. It's a pity because I'm generally very interested in the topics discussed here, but the tone and the underlying ideology is pushing me away.

I would be very interested in hearing elaboration on this topic, either publicly or privately.

Surprised by either. Just finding a structure of causality that was very unexpected. I agree the intuition could be built from other sources.

I think having the concept of PCAs prevents some mistakes in reasoning on an intuitive day to day level of reasoning. It nudges me towards fox thinking instead of hedgehog thinking. Normal folk intuition grasps at the most cognitively available and obvious variable to explain causes, and then our System 1 acts as if that variable explains most if not all the variance. Looking at PCAs many times (and being surprised by them) makes me less likely to jump to conclusions about the causal structure of clusters of related events. So maybe I could characterize it as giving a System 1 intuition for not making the post hoc ergo propter hoc fallacy.

Maybe part of the problem Jonah is running in to explaining it is that having done many many example problems with System 2 loaded it into his System 1, and the System 1 knowledge is what he really wants to communicate?

See Rationality is about pattern recognition, not reasoning.

Your tone is condescending, far outside of politeness norms. In the past I would have uncharitably written this off to you being depraved, but I've realized that I should be making a stronger effort to understand other people's perspectives. So can you help me understand where you're coming from on an emotional level?

Why did you have this impression?

Groupthink I guess: other people who I knew didn't think that it's so important (despite being people who are very well educated by conventional standards, top ~1% of elite colleges).

Tell me how exactly you're planning to use PCA day-to-day?

Disclaimer: I know that I'm not giving enough evidence to convince you: I've thought about this for thousands of hours (including working through many quantitative examples) and it's taking me a long time to figure out how to organize what I've learned.

I already have been using dimensionality reduction (qualitatively) in my day to day life, and I've found that it's greatly improved my interpersonal relationships because it's made it much easier to guess where people are coming from (before people's social behavior had seemed like a complicated blur because I saw so many variables without having started to correctly identify the latent ones).

i think the sooner you lose the need for everything to resonate deeply or have a concise insightful summary, the better.

You seem to be making overly strong assumptions with insufficient evidence: how would you know whether this was the case, never having met me? ;-)

"impression that more advanced statistics is technical elaboration that doesn't offer major additional insights"

Why did you have this impression?

Sorry for the off-topic, but I see this a lot in LessWrong (as a casual reader). People seem to focus on textual, deep-sounding, wow-inducing expositions, but often dislike the technicalities, getting hands dirty with actually understanding calculations, equations, formulas, details of algorithms etc (calculations that don't tickle those wow-receptors that we all have). As if these were merely some minor additions over the really important big picture view. As I see it this movement seems to try to build up a new backbone of knowledge from scratch. But doing this they repeat the mistakes of the past philosophers. For example going for the "deep", outlook-transforming texts that often give a delusional feeling of "oh now I understand the whole world". It's easy to have wow-moments without actually having understood something new.

So yes, PCA is useful and most statistics and maths and computer science is useful for understanding stuff. But then you swing to the other extreme and say "ideas from advanced statistics are essential for reasoning about the world, even on a day-to-day level". Tell me how exactly you're planning to use PCA day-to-day? I think you may mean you want to use some "insight" that you gained from it. But I'm not sure what that would be. It seems to be a cartoonish distortion that makes it fit into an ideology.

Anyway, mainstream machine learning is very useful. And it's usually much more intricate and complicated than to be able to produce a deep everyday insight out of it. I think the sooner you lose the need for everything to resonate deeply or have a concise insightful summary, the better.

Depending on the country there are legal and not-so-legal methods to get better healthcare. You could buy a better package legally, put some cash in the doctor's coat, etc.

It's quite easy to get more expensive healthcare. On the other hand that doesn't mean the healthcare is automatically better.

If you are willing to pay for any treatment out of your own pocket then a doctor can treat you in a way that's not being payed for by an insurance company because it's not evidence-based medicine.