My default assumption for any story that ends with "And this is why our ingroup is smarter than everyone else and people outside won't recognize our genius" is that the story is self-serving nonsense, and this article isn't giving me any reason to think otherwise.

A "userbase with probably-high intelligence, community norms about statistics and avoiding predictable stupidity" describes a lot of academia. And academia has a higher barrier to entry than "taking the time to write some blog articles". The average lesswrong user doesn't need to run an RCT before airing their latest pet theory for the community, so why would it be so much better at selectively spreading true memes than academia is?

I would need a much more thorough gears-level model of memetic spread of ideas, one with actual falsifiable claims (you know, like when people do science) before I could accept the idea of LessWrong as some kind of genius incubator.

Claim: memeticity in a scientific field is mostly determined, not by the most competent researchers in the field, but instead by roughly-median researchers. [...] Sure, the most competent people in the field may recognize the problems, but the median researchers don’t, and in aggregate it’s mostly the median researchers who spread the memes.

This assumes the median researchers can't recognize who the competent researchers are, or otherwise don't look to them as thought leaders.

I'm not arguing that this isn't often the case, just that it isn't always the case. In engineering, if you're more competent than everyone else, you can make cooler shit. If you're a median engineer trying to figure out which memes to take on and spread, you're going to be drawn to the work of the more competent engineers because it is visibly and obviously better.

In fields where distinguishing between bad research and good research has to be done by knowing how to do good research, rather than "does it fly or does it crash", then the problem you describe is much more difficult to avoid. I argue that the difference between the fields which replicate and those which don't is as much about the legibility of the end product as it is about the quality of the median researcher.

Curated. I think this model is pretty useful and well-compressed, and I'm glad to be able to concisely link to it.

Insofar as it is accurate, the policy implications are still much open to debate, for here on LessWrong and for other ecosystems in the world.

People did in fact try to sound the alarm about poor statistical practices well before the replication crisis, and yet practices did not change, 

This rings painfully true. As early as the late 1950s, at least one person was already raising a red flag about the risks that psychology^[1] might veer into publishing a sea of false claims:

There is some evidence that in fields where statistical tests of significance are commonly used, research which yields nonsignificant results is not published. Such research being unknown to other investigators may be repeated independently until eventually by chance a significant result occurs—an 'error of the first kind'—and is published. Significant results published in these fields are seldom verified by independent replication. The possibility thus arises that the literature of such a field consists in substantial part of false conclusions resulting from errors of the first kind in statistical tests of significance.

1. ^{^}

   Sterling isn't explicitly talking about psychology, but rather any field where significance tests are used.

community norms which require basically everyone to be familiar with statistics and economics

I disagree. At best, community norms require everyone to in principle be able to follow along with some statistical/economic argument. 
That is a better fit with my experience of LW discussions. And I am not, in fact, familiar with statistics or economics to the extent I am with e.g. classical mechanics or pre-DL machine learning. (This is funny for many reasons, especially because statistical mechanics is one of my favourite subjects in physics.) But it remains the case that what I know of economics could fill perhaps a single chapter in a textbook. I could do somewhat better with statistics, but asking me to calculate ANOVA scores or check if a test in a paper is appropriate for the theories at hand is a fool's errand. 

I think the central claim is true in some fields and not particularly true in others. But one variation of the claim that interests me is, if we consider memeticity relative to public opinions/awareness, then I agree with this more strongly, where the opinions of median researchers are disproportionately more popular than more correct opinions. In some sense, I think the "most correct opinions" are basically never the most popular to the public, though perhaps I wouldn't invoke the median researcher problem to explain this edge case, but a more specific hypothesis instead.

I mostly disagree with the final paragraph about LW. To me it seems that 1) opinions on LW are quite diverse 2) LW opinions are quite plausibly worse than average in some fields, e.g. mental health, and 3) there's very little evidence to suggest that high intelligence leads to correct/rational opinions in the context of complicated beliefs about the world; people with high IQ are just as susceptible to converging on "easily disprovable" opinions, often they're just better at deceiving themselves and others using rationalization.

I happened to read a Quanta article about equivalence earlier, and one of the threads is the difficulty of a field applying a big new concept without the expository and distillation work of putting stuff into textbooks/lectures/etc.

That problem pattern-matches with the replication example, but well-motivated at the front end instead of badly-motivated at the back end. It still feels like exposition and distillation are key tasks that govern the memes-in-the-field passed among median researchers.

I strongly suspect the crux of the replication crisis example is that while there are piles of exposition and distillation for probability/statistics, they are from outside whatever field experiences the problem, and each field needs its own internal completion of these steps for them to stick.

I think this should be a third specialization for every scientific field. We have theorists, we have experimentalists, and to this we should add analysts. Their work would specialize in establishing the statistical properties of experimental designs and equipment in the field on the one side, and the statistical requirements to advance various theories on the other side.

I think one thing that's missing here is that you're making a first-order linear approximation of "research" as just continually improving in some direction. I would instead propose a quadratic model where there is some maximal mode of activity in the world, but this mode can face certain obstacles that people can remove. Research progress is what happens when there's an interface for removing obstacles that people are gradually developing familiarity with (for instance because it's a newly developed interface).

Different people have different speeds by which they reach the equillibrium, but generally those who have an advantage would also exhibit an explosion of skills and production as they use their superior understanding of the interface.

Prototypical example: imagine a scientific field in which the large majority of practitioners have a very poor understanding of statistics, p-hacking, etc. Then lots of work in that field will be highly memetic despite trash statistics, blatant p-hacking, etc. Sure, the most competent people in the field may recognize the problems, but the median researchers don’t, and in aggregate it’s mostly the median researchers who spread the memes.

Complicated analysis (like going far beyond p-values) is easy for anyone to see and it is evidence of effort. Complex analysis usually coocurs with thoroughness so fewer mistakes. Complicated analysis coocurs with many concurrent tests so less need to produce positive results so less p-hacking. Consequently, there is a fairly simple solution to researchers with mediocre statistical skills gaining too much trust: more plots! Anyway, I find correlation graphs and multiple comparison impressive. Also I am usually more skilled in data analysis than the subject of a paper so can more easily verify that. 

How do you think competent people can solve this problem within their own fields of expertise? 

For example, the EA community is a small & effective community like you've referenced for commonplace charity/altruism practices. 

How could we solve the median researcher problem & improve the efficacy & reputation of altruism as a whole?

Personally, I suggest taking a marketing approach. If we endeavor to understand important similarities between "median researchers", so that we can talk to them in the language they want to hear, we may be able to attract attention from the broader altruism community which can eventually be leveraged to place EA in a position of authority or expertise.

What do you think?

Well, I hope that the self-importance shown in this post is not a true reflection of the community; although unfortunately I think it might well be.

Spurious correlation here, big time, imho.

Give me the natural content of the field and I bet I easily predict whether it may or may not have replication crisis, w/o knowing the exact type of students it attracts.

I think it's mostly that the fields where bad science may be sexy and less-trivial/unambiguous to check, or, those where you can make up/sell sexy results independently of their grounding, may, for whichever reason, also be those that attract the non-logical students.

Agree though with the mob overwhelming the smart outliers, but I just think how much that mob creates a replication crises is at least in large part dependent on the intrinsic nature of the field rather than due to the exact IQs.

We argue that memeticity—the survival and spread of ideas—is far more complex than the influence of average researchers or the appeal of articulate theories. Instead, the persistence of ideas in any field depends on a nuanced interplay of feedback mechanisms, boundary constraints, and the conditions within the field itself.

In fields like engineering, where feedback is often immediate and tied directly to empirical results, ideas face constant scrutiny through testing and validation. Failed practices here are swiftly corrected, creating a natural selection process that fosters robustness. Theories in engineering and similar disciplines rely on mathematical modeling to bridge concepts with real-world outcomes. This alignment between model and outcome isn’t instantaneous, but the structural setup of the field encourages what we might call “antifragility”: ideas that survive these feedback loops emerge stronger and more reliable, not solely because of the competence of individual researchers but because of the field’s built-in corrective pressures.

In contrast, fields like social sciences or linguistics often lack such direct empirical anchors. Theories in these areas can persist on the basis of articulation, cultural resonance, or ideological alignment, sometimes for decades. The classic linguistic theories of the 1970s, for instance, endured largely because they fit well within the intellectual climate of the time, with little empirical scrutiny available to challenge their assumptions. Without rigorous feedback, such theories may linger, shaping academic thought without the resilience-testing that empirical pressure imposes.

The emergence of large language models (LLMs) introduces a new dimension of feedback in these traditionally insulated fields. LLMs can analyze extensive linguistic and behavioral data, revealing patterns that either align with or contradict established theories. This new capacity acts as an initial “stress test” for long-standing ideas, challenging assumptions that may have previously gone unexamined. However, while LLMs provide valuable insights, they are not infallible arbiters of truth. Their analysis depends on training data that can inherit biases from past frameworks, so they function as a starting point rather than a comprehensive solution. The true rigor of empirical validation—akin to engineering’s feedback loops—remains essential for developing resilient theories.

In summary, the memetic success of ideas depends not just on the competency or articulacy of individual researchers but on how effectively feedback mechanisms, field boundaries, and empirical standards shape those ideas. Fields with strong, built-in corrective feedback—often mathematically modeled—are inherently more resilient to the persistence of weak ideas. Fields without such constraints are vulnerable to influence by articulation alone, creating environments where ideas can thrive without robust validation. The introduction of LLMs offers a valuable corrective force, but one that must be used with awareness of its limitations. By integrating empirical rigor and maintaining reflective practices, disciplines across the spectrum can ensure that memeticity aligns more closely with resilience, rather than rhetorical appeal alone.