之所以OUT
是因为没看延禧攻略
在统计学中，我们需要研究两个变量之间的关系，然后可以推算出两个变量之间关系的model模型表达式，进而可以进行预测估算。
但是对于两个变量之间的具体关系，其实常常会考到一个知识点。
那就是correlation and causation的区分。
第一点：
Correlation shows how closely two variable vary with each other. For example, as the value of one increases, so does the other.
两个变量之间只是数量上面存在某种关系。
Causation is when two variables directly affect each other.
For example, the time you go to bed affects the number of hours you sleep.
两个变量之间会相互影响，存在某种因果关系。
Sometimes a cause and effect are closely related, but not always. It is easy to assume that events that are closely correlated are also connected causally. But correlation between two events does not mean that one has caused the other.（有相关性并不代表有因果关系）
第二点：
那么相关性和因果关系到底怎么确定呢，
有些可以根据生活常识或者科学原理去得到结论，
但是大部分情况其实我们的主观意识不一定是正确的，数据也不能单方面的得到因果关系，
比如，气温的高低和股票指数也许凑巧就有正相关或者负相关的关系，
但是两者之间根本没有任何关系，除了数量上看起来的凑巧关系，
因为有可能有其他隐形要素在影响，
这时候就需要借助experiment试验的严密设计，才能真正去验证。
「CORRELATION NEVER PROVE CAUSITION」
Experimental research investigates what happens when you change a variable, for example what happens to a liquid when you increase the temperature.
Correlated research does not change the variables. It observes the outcome of two events and offers statistical data as proof.
我没看延禧攻略，那就out了吧汪汪汪
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