Great article from Experimental History, “Nothing ever dies. It merely becomes embarrassing:”
Here’s a reasonable thought: as the replication crisis has unfolded over the past 10-15 years, a bunch of psychological phenomena have been debunked and discarded forever. Power posing, ego depletion, growth mindset, stereotype threat, walking slower after reading the word “Florida”—all gone for good
To beat the dead horse about how “studies do not reproduce,” what if there were a way to guarantee results are always reproducible? That already exists–it’s called math. Except for a small subset of math called “experimental mathematics,” math results are always true. It’s like, “Why can’t we hold an empirical science to the same rigor as math,” and it’s because they are not the same thing?
While it’s possible to reproduce a math result in a reasonable timeframe, typically 3-6 months, this is not practical for studies, which can run for years and involve many subjects. “Growth mindset” cannot be refuted in the same way a math conjecture can be disproven–only that evidence can be marshalled against it, and it’s up to society to decide to abandon it. The referees can only check if the data and methodology appear sound, not if the study is true per se.
Regarding the epidemic of bad studies, the problem has to do with incentive structures that favors quantity over quality, and a credulous media all too willing promote studies that affirm popular narratives.
IQ studies, such as twin studies, are scrutinized with a fine-tooth comb, whereas weaker ideas such as “growth mindset” or “power posing” face much less scrutiny. As a result, we can be confident IQ research aligns more closely with reality, as it’s so heavily vetted. There is so much at stake when it comes to IQ. Entire social institutions are predicated on the belief that people are inherently equal or can be made equal. IQ research runs counter to this, and is therefore attacked.
Or the belief that “better schools and teachers” or “more accountability” will lead to better academic outcomes. As Freddie deBoer has said at length, “…the evidence is very strong: once you correct for pre-entry ability, such schools do not offer any quantitative advantage over regular high schools from the same cities or districts. There are a variety of ways to assess these things and a variety of outcome variables, but the research points in only one direction.”
Individual differences of innate ability persist despite overall gains in academic achievement. It’s impossible to create an academic intervention that eradicates such differences without also holding back smarter people. All intervention does is reveal inherent differences of ability. Some examples are high-stakes math competition prep, or studying for the the SATs or the LSATs: more studying raises average scores, but the smarter people will still score higher, as they are practicing, too.
If popular topics such as AI, politics, education, or psychology seem especially gripped by dogmas, nutrition research and dieting are even worse. It took half a century for the tide to finally turn against the prevailing dogma that blamed heart disease on saturated fat. Other dogmas include that “slow or fast metabolisms do not exist” (they do). Or that “exercise is effective for weight loss” (at best it’s mixed). Or that “low fat diets are better for health or losing weight” (the evidence again is inconclusive).
Consider the paper below, which went viral on Twitter:
Two economists just published a mathematical proof that AI will destroy the economy.
Not might. Not could. Will — if nothing changes.
The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed.… pic.twitter.com/Owkd98RdbC
— Elias Al (@iam_elias1) April 29, 2026
Ahh, yes, the “AI will destroy the economy” lemma–a standard result in any college math textbook. Social scientists have been invoking math since the 1800s (hello Malthus) to make wrong predictions. These models work in theory on paper, but fail in the real world. Systems and models are useful–I use them myself when it comes to investing and forecasting–but they do not work here. That so many supposed “educated” people accept such dubious findings uncritically, explains why bad social science research persists in the public consciousness.
It was the understanding of germ theory and the consequent abandonment of “miasma theory,” that finally stemmed hospital deaths, for example. When doctors started sterilizing instruments and washing their hands, deaths plunged. In the end, stubborn attachments are bad because they impede understanding and prevent the adoption of solutions . It’s not only that the idea is bad per se, but that it also crowds out the marketplace for better ones.