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Ok, correlations and causation footnotes:
好的，相关性和因果关系脚注：
In the main video I said that when you find a correlation,
在视频中，我说，当你发现一个相关性，
it's natural to look for explanations or causes of it.
很自然地会找到相关的解释或原因。
This is called Reichenbach's Principle.
这被称为赖兴巴赫原理。
But sometimes correlations occur just by chance,
但有时相关性只是偶然发生的，
like those on the website "spurious correlations"
就像网站上的“虚假相关性”，
which selectively cherry-picks data points from different stats that randomly happen to line up.
选择性地从随机排列的不同统计数据中挑选数据点。
As an example of a chance correlation, if I flip two coins enough times,
作为一个概率关联的例子，如果我多次掷两个硬币，
eventually there'll be a long string of matching heads or tails just by chance,
最终将偶然碰到一长串匹配后的正面或反面，
and if I just cherrypick those flips I can make it look like the coins are super correlated.
如果我只是挑剔那些翻转，我可以使硬币看起来是非常具有相关性的。
But when an apparent correlation is actually random in origin (like in this case),
但是，当一个明显的相关性实际上是随机的（就像这个例子），
then if you keep looking at larger and larger samples, the correlation should go away.
那么如果你继续观察越来越大的样本，相关性就会消失。
This is it sometimes looks like particle physicists have discovered a new particle,
有时候看起来粒子物理学家发现了一个新粒子，
only for that to go away when they collect more data.
但当他们收集更多的数据时，这些粒子就会消失。
Also, you may have noticed there was no mention of feedback loops in the main video
此外，您可能已经注意到在视频中没有提到反馈循环
that's because, from a causal point of view, feedback loops,
这是因为，从因果关系的角度来看，反馈循环，
like how more grass means more sheep, means less grass means less sheep, means more grass and so on.
就像草越多就意味着羊越多，草越少就意味着羊越少，等等。
from a causal point of view, this isn't actually a loop.
从因果关系的角度来看，这实际上不是一个循环。
It's more of a chain, where the amount of grass and sheep now affect the amounts of grass and sheep next year,
它更像是连锁反应，现在草和羊的数量会影响到明年的草和羊的数量，
and the year after and so on, so from year to year there's feedback
以此类推等等，所以每年草的数量和羊的数量之间会有反馈，
between the amount of grass and the amount of sheep which we kind of draw as a loop,
我们把它作为一个循环，
but the causal relationship always goes from the present to the future,
但是因果关系总是关联着现在到未来。
which we should draw as some sort of spirally helix thing.
我们应该画成螺旋状的东西。
