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  • Ok, correlations and causation footnotes: In the main video I said that when you find

  • a correlation, it's natural to look for explanations or causes of it. This is called Reichenbach's Principle.

  • But sometimes correlations occur just by chance, like those on the websitespurious correlations

  • which selectively cherry-picks data points from different stats that randomly happen

  • to line up.

  • As an example of a chance correlation, if I flip two coins enough times, eventually

  • there'll be a long string of matching heads or tails just by chance, and if I just cherrypick

  • those flips I can make it look like the coins are super correlated.

  • But when an apparent correlation is actually random in origin (like in this case), then

  • if you keep looking at larger and larger samples, the correlation should go away.

  • This is it sometimes looks like particle physicists have discovered a new particle, only for that

  • to go away when they collect more data.

  • Also, you may have noticed there was no mention of feedback loops in the main videothat's

  • because, from a causal point of view, feedback loops, like how more grass means more sheep

  • means less grass means less sheep means more grass and so onfrom a causal point of

  • view, this isn't actually a loop.

  • It's more of a chain, where the amount of grass and sheep now affect the amounts of

  • grass and sheep next year, and the year after and so on, so from year to year there's

  • feedback between the amount of grass and the amount of sheep which we kind of draw as a

  • loop, but the causal relationship always goes from the present to the future, which we should

  • draw as some sort of spirally helix thing.

Ok, correlations and causation footnotes: In the main video I said that when you find

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B1 grass sheep correlation feedback particle loop

Misconceptions Footnote †: Randomness and Feedback

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    Summer posted on 2021/04/01
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