有的时候因为representative效应，我们会把一个概率更小的事情当作更容易发生的事情。比如书里面linda的实验，对linda的介绍是这样的：
“Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations.”
Excerpt From: Daniel Kahneman. “Thinking, Fast and Slow.” iBooks.
然后给出了下面几个选项：
“Linda is a teacher in elementary school.
Linda works in a bookstore and takes yoga classes.
Linda is active in the feminist movement.
Linda is a psychiatric social worker.
Linda is a member of the League of Women Voters.
Linda is a bank teller.
Linda is an insurance salesperson.
Linda is a bank teller and is active in the feminist movement.”
最后一条明显在概率上要比倒数第三条低，因为linda既是bank teller 又是feminist movement的概率肯定要比linda是bank teller要低（因为linda是bank teller的概率囊括了linda既是bank teller，又是feminist movement）但是问题是当我们看到linda的个人介绍的时候，我们看到她特别为人权社会公平和歧视担忧。（As a student, she was deeply concerned with issues of discrimination and social justice, ）所以这时候representative的效果就显示出来了，我们系统一会不加思考的认为linda既是bank teller又是feminist movement的概率要比linda是bank teller的概率要高，因为我们脑子里的系统一自动给feminist movement加了权重，因为它更符合linda在我们脑子里的印象。这其实就是一种偏见（bias）。
“The most coherent stories are not necessarily the most probable, but they are plausible, and the notions of coherence, plausibility, and probability are easily confused by the unwary.”
这句是重点，能够形成一致性的并不是最有可能发生的，我们的大脑倾向于在一些随机的事件中寻找一些有相关性、或者说能够形成一致性（coherent）的条件。有些描述，比如linda特别注重人权，这并不代表她非得要是bank teller的同时又是feminist。虽然这是一种可能，但是满足两个条件的概率一定要比满足前者要小（bank teller 的概率范围包含了bank teller & feminist ）。所以我们的系统一在看到一个一致性比较强的两个条件的时候就会自动把它们联系起来，尽管有时候这两个并不一定真的同时被满足。这时候我们的判断或者决策就会不知不觉的被影响了。
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