Earlier quoted context omitted.
You'd think so. I used to believe this too. Surely you try and build on a result which creates an implicit replication study, then your experiments don't work and the problem is discovered pretty quick. Hence, self correction. It often doesn't happen. The reasons seem to vary by field. We do see it in a few very hard fields like materials science. In the softer end of social sciences like anthropology, education or t…
Whilst I weakly agree with this characterisation, it's not at all fair or accurate to put epidemiology and climatology in the same category as your other examples. Yes they have some weaknesses in replication practices (especially for purely computational work), yes the feedback loop for self-correction can be slow, but it would be a mistake to conclude or imply that the major results of those fields are therefore un…
Modern (computational) epidemiology is rife with unscientific practices. They ignore data that shows a model was invalidated so the fact they make testable predictions isn't really useful. They also engage in a lot of circular reasoning, buggy coding and logical fallacies. During COVID I wrote a whole report on this topic for some politicians [1]. But the biggest issue is "A problem in theory" again - epidemiologists conflate fitting a curve in R with developing a hypothesis, so the field is overrun with overfit models that aren't based on any refinable theory of disease, just misuses of statistics. Even if you prove a paper's predictions were wrong it changes nothing because nothing built on it anyway.
The problem in climatology is that when the models don't fit the data they just change the data and claim victory, e.g.
September 2013: https://www.spiegel.de/international/world/climate-scientist...
June 2015: https://www.nature.com/articles/nature.2015.17700