March 2013
Beginner
256 pages
6h 33m
English
Content preview from Behavioral Finance
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CHAPTER 12
Causality and Statistics
In each of our daily lives, we face a myriad of instances of using data from past observations to draw conclusions about particular hypotheses. From deciding what tie to wear for an interview, or how to approach a friend or colleague to ask a question, or even what to eat for breakfast in the morning, we are taking past data on similar events and trying to ascertain specific details about the given situation.
The process humans use to draw conclusions based on observable data has not evolved in a flawless way. We often overweight particular data points and underweight others in ways that can lead us to draw misinformed conclusions. Some of these mistakes can be rather innocuous, while others can lead to catastrophic outcomes.
In this chapter, we discuss some of the errors people commonly make when drawing conclusions from sets of information, as well as the potential consequences of some of these actions.
REPRESENTATIVENESS
Representativeness reflects the tendency of most people to read too much into stereotypes. As an example, consider the following problem, taken from Kahneman:1
Tom W. is a graduate student at the main university of your state. Please rank the following disciplines in order of likelihood that Tom is now a student in each field:
- Business Administration
- Computer Science
- Engineering
- Humanities and Education
- Law
- Medicine
- Library Science
- Physical and Life Science
- Social Science and Social Work
Most ranked the disciplines in size ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.