Trading Psychology | Representativeness Bias – The Dangers of a Small Sample Size

In order to derive meaning from life experiences, people have developed an innate propensity for classifying objects and thoughts. When they confront a new phenomenon that is inconsistent with any of their preconstructed classifications, they subject it to those classifications anyway, relying on a rough best-fit approximation.

 

Representativeness Bias – The Dangers of a Small Sample Size

 

There are two main types of representativeness bias, namely (i) base-rate neglect and (ii) sample-size neglect. We will focus on the latter, since it occurs more frequently in trading.

In sample-size neglect, traders, when judging the likelihood of a particular trade outcome, often fail to accurately consider the sample size of the data from which they base their judgments. They incorrectly assume that small sample sizes are representative of populations. This is also known as “the law of small numbers”.

This problem is observed when traders try to backtest systems by using small sample sizes of data, and extrapolate their favourable results. However, these results are most likely not representative of the effectiveness of the system. This is a common tactic applied in marketing gimmicks.

Another common phenomenon has to do with hot tips. For example, you might hear someone say “my broker gave me three great stock picks over the past month, and each stock is up by over 10%”. While this is enough to sway most people, thinking that the broker is a genius, this assessment is based on a very small sample size.

What is the best solution for this?

Cheers
Spencer
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Spencer Li is a trader, investor & entrepreneur who achieved financial freedom at 27 (2013), having to date accumulated a diversified portfolio of properties, stocks, REITs, and 10+ businesses across different industries. As a professional trader, he has over 10 years of market experience, and has been featured on more than 20 occasions in the media.

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