Statistics are meaningless without a rigorously examined causal model of the phenomenon under investigation. In my experience of statistics education, the art of crafting causal theories was scarcely addressed.
A classic example I recall is a Feynmann story, where a group of researchers were getting very statistically sound and repeatable results of very unusual and unexplained particle track behavior in a cloud chamber. Feynmann looked at the data and said "you probably have a tiny piece of metal in the cloud chamber somewhere" and that turned out to be the explanation.
Similar examples in the social sciences include systematic bias in the preparation and administration of IQ tests to different groups of people (see Charles Murray's 'Bell Curve' vs. Stephen J. Gould's 'The Mismeasure of Man').
Hundreds of other examples can be found across all scientific disciplines, unfortunately. To quote the smartest PI I ever worked for "There's a lot of BS in statistical analysis".
It seems like there's this extreme reaction against people behaving like correlation equals causation, but instead of over-emphasizing correlation, it gets dismissed entirely.
Nevertheless, the fallacy is so commonplace. You will easily find a seemingly educated person selectively balking at the notion that causal relationship is ultimately a conjecture or at the notion that causal relationship is possible, depending on their pre-existing beliefs.
Being emotionally attached to purported causal relationship X->Y, they will count all correlational evidence in favor; when pointed out that the evidence is correlational, they will wave it off with more correlational evidence.
If that causal relationship does not happen to align with their world view, of course, they will be right onto you with the old correlation-does-not-imply-causation mantra.
Ruling out chance through significance and power, what phenomenon in the world is correlated without being causally related _somehow_?
Repeat every week with the worst traffic intersection for accidents.
What you find is. IT WORKS! HURRAH! These intersections are more than usually not the worst the following week! The evidence is clear. The correlation is utterly compelling. It is significant. It has power. How could it possibly be unrelated?
Now if we stop it being comically silly in our example and make it a red light & speed camera, see how the issue is much more difficult. There is a clear line of potential causation of fixing dangerous intersections. But is it really better than folk dancing for 20 minutes at 2am? [1]
[1] This example should not be interpreted as being an opposition to all red light and speed cameras.
So in other words, several of the commenters here are right. On the one hand we shouldn't jump to conclusions, but on the other hand we should listen to the clues.
There's billions of possible variables. There's N^2 possible pairs of variables. It's not feasible to look at every pair that is correlated.
so, everything defying common sense should be reviewed, to find why and how exactly is this happening.
Not really.
If we can get there, then we can talk about what we can do to improve things from there.
Yes, I agree, and this is precisely my point. My experience with introductory stats was a heavy focus on the technical details, when in fact what would be more effective is focusing on statistical logic.
I’m specifically thinking that something like Judea Pearl’s The Book of Why would be good to introduce early in stats education.
I think a lot of it is more ideological at the "average" citizen level - on both sides. Most people aren't looking at the data, they're believing whatever they're told that aligns with their personal beliefs.
I don't know, most of the misinformation on the matter I've seen is just flat out wrong. Not misinterpreting statistics incorrect, just flat out lying, using false numbers or statements, etc. Having a cursory knowledge of statistics won't help you if you're incapable of Googling to check if a statistic is true or not.