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Scientists are very frequently wrong. Any scientist who doesn't admit that is none. One example is adjacent to the topic here, continued adherence to the amyloid beta hypothesis.

But their mistakes are generally subtle, and not what somebody who knows nothing about the subject invents in the first seconds after first hearing about the topic.

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We're talking about epidemiology though. Their papers routinely contain major, grievous errors that anyone can spot in five minutes. There might well be subtle errors too but it doesn't matter when the field is overrun with papers that are quite obviously invalid on their face to any outsider, yet none of the insiders care.

The primary COVID model that triggered lockdowns was full of programming errors. It had never been peer reviewed, and its prediction of deaths varied by 80,000 depending on whether you engaged a data loading optimization or not. It gave totally different results depending on available CPU features! There were no tests and the results had never been validated against anything. Outsiders pointed out these problems, and the team didn't care, nor did anyone else in the field of epidemiology.

That's just one example of many. Epidemiology is kinda like the phrenology of our era (one of quite a few). It's not built on a firm scientific foundation.

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