Chapter 10 – The Representativeness Heuristic
Heuristics are general rules of thumb that help people arrive at their decisions. However, they
can lead to systematic biases.
Representativeness Heuristic – People often judge probabilities by the degree to which A
resembles B.
E.g. Linda is a bank teller, or bank teller and a feminist. People violate a rule of probability,
because her being a bank teller is more likely than both a bank teller and a feminist. This is
called the conjunction fallacy.
As the amount of detail increases, the probability decreases, but the representativeness and
apparent likelihood may increase. More detail = makes more sense, and more
representative of how we imagine particular events.
The Law of Small Numbers – belief that random samples of a population will resemble each
other and the population more closely than statistical sampling would predict.
E.g. Mean IQ is 100, and out of 50 kids, one person received 150. What is the mean IQ of the
sample? Although it‟s most likely 101, many people still guess 100, because they assume a
lower score will even out the 150 – but this assumes that chance is self-correcting. It is not,
and this assumption is a bias from the representativeness heuristic.
Gambler‟s Fallacy – the belief that a successful outcome is due after a run of bad luck.
Hot Hand – the belief that a success leads to more successes and a miss leads to more misses.
Neglecting Base Rates – In one experiment, descriptions of people were given, and they were
rated as an engineer or lawyer, given there are 30 engineers and 70 lawyers. At the end,
they asked the probability that a random person was an engineer, and most people chose
30%. However, if you add in the random person with a random description, people forgot the
base rate and said 50% - he is equally as likely to be an engineer as a lawyer.
People use base rates when the information was causal rather than non-causal or unrelated.
Nonregressive prediction – e.g. If test scores are moderately representative of GPA, and
someone got a 725/800, what is their GPA? Most people would correlate it with a 3.5 GPA,
but only if it was PERFECTLY representative. Due to regression, it should be between the
average (2.5) and the perfect (3.5). On the basis of regression, outstanding performance is
usually followed by average performance. Winning and then losing is not caused by a jinx,
but rather a regression to the mean.
Conclusion: Don‟t be misled by highly detailed scenarios
Whenever possible, pay attention to base rates
Remember that chance is not self-correcting
Don‟t misinterpret regression toward the mean Chapter 11 – The Availability Heuristic
Availability Heuristic – rule of thumb in which decision makers assess the frequency of a class
or the probability of an event by the ease these instances can be brought to mind.
Biases occur when people believe events are more frequent due to the ease of thinking, rather
than actual probability. E.g. more likely to be killed by falling airplane parts or a shark? We
say shark because

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