Flu vaccination linked to 40% reduced risk of Alzheimer’s disease

1. The journal article didn't suggest it was causal. But such a correlation with such a large population warrants publication and further research into causation.

2. literally the first thing that any epidemiologist would consider is potential confounders. There is a big list of covariates they included into their model here: https://content.iospress.com/articles/journal-of-alzheimers-...

There are quite a few things that can be done to alleviate potential false correlations: DAGs, prior literature, removing confounders, and including covariates are all things at disposal.

3. Such a large sample size + previously reported findings + an inclusion of enough covariates still doesn't == causation, BUT it's important to publish and shout about so we can then look into the potential biological underpinnings that may cause this. Which by the way, those experiments may still use data science techniques.

4. If you are actually interested, there is a whole topic of this called 'causal inference' with one famous criteria list called the 'Bradford Hill Criteria': https://en.wikipedia.org/wiki/Bradford_Hill_criteria. This list is often argued about.

5. If all of this information was new to you, please stop spouting 'correlation != causation'. You probably don't know as much as you think

I feel like in English, "linked" usually implies some sort of potential causation, not just a general relationship. For example: "boyfriend linked to murder case".

Your misplaced trust in your own plausibility filter causes you to make foolish posts on HN.

I find it plausible that the few people who spent hours reviewing this paper didn't think of that, and that the people publishing the paper ignored it since ignoring it could help them gain more funding.

> Your misplaced trust in your own plausibility filter causes you to make foolish posts on HN.

It is foolish to believe that scientists does things properly. As an outsider you only see the outlier results, meaning that most of what reaches HN could be faked or statistical trickery or have simple explanations due to poor science since poor science is more likely to produce headlines, while most scientists could still do the right thing.

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.