11. Reasoning, Judgment, and Choice

Question 1

SYLLOGISM

words that are capitalized are subsections in this chapter

Answer 1

A logical argument that consists of two premises and a conclusion. Each premise describes a relationship between two categories.

Question 2

Universal Premise

Can this be inverted?

Answer 2

A premise that applies to all in a category.
“All cows eat grass”.

Can’t be inverted. “All grass is eaten by cows” is not necessarily true.

Question 3

Particular Premise

Can this be inverted?

Answer 3

A premise that applies to the logical “some”.
The logical “some” simply means “greater than zero”

Sometimes. Because of the “some” operator, this argument can be interpreted in three different ways.

Question 4

Affirmative Premise

Answer 4

A premise that states something has a property. This can be thought of as a substate for Universal and Particular premises.

Question 5

Negative Premise.

Answer 5

A premise that states something doesn’t have a property.

Question 6

Heuristic

Answer 6

A mental rule of thumb that saves energy. It often yields the right answer. Not always. Sometimes it misleads us.

Question 7

Bias

Answer 7

A tendency to see a situation a certain way.

Question 8

INTUITIVE STATISTICS (We saw eight!)

Answer 8

The collection of heuristics and biases that we use to make sense of our world. They work sometimes, but they’re super flawed.

Question 9

The law of large numbers

Answer 9

An actual statistical concept! The larger our sample, the closer it approaches the true value.

Question 10

The law of averages

Answer 10

Should be called the fallacy of averages, in my opinion. Our tendency to believe that if our samples aren’t behaving the way we expect them to, the likelihood that they’ll come around is increasing.

Question 11

The law of small numbers

Answer 11

DAMMIT KAHNEMAN AND TVERSKY! THESE ARE FALLACIES.
The mistaken belief that a small sample should reflect the entire population.

For example, if McGil is 50% male and 50% female, and we selected 30 people randomly, we expect 15 of them to be male. In reality, we can’t say anything about the distribution.

Question 12

Representativeness heuristic

Answer 12

A heuristic where we assume that small samples resemble each other and the population from which they are drawn.

I guess this is often the case, but sometimes it’s not.

Question 13

Adjustment and Anchoring

Answer 13

A phenomenon, where people’s judgments of magnitude are biased based on the initial value they see.

If you want people to give you money, make sure to hit them hard first! And then give them an easy way out.

Question 14

Availability Heuristic

Answer 14

The belief that the more easily you can think of examples of something, the more frequently the event occurs.

Like dad and his belief that we’re always on the computer.

Question 15

Illusory Correlation

Answer 15

The mistaken belief that events go together when in fact they don’t.

Question 16

Recognition heuristic

plus a demonstration!

Answer 16

An idea proposed by Gigerenzer and Goldstein, when they saw how savage Tversky and Kahneman were towards our rational ability.

The recognition heuristic is when participants choose something they recognize over something they don’t. Turns out this is ecologically rational!

Interestingly, Gigerenzer did a study on American students vs German students on which was bigger - San Antonio or San Diego. German students were able to get it right 100% of the time, using the recognition heuristic.

Question 17

Ecological rationality

Answer 17

When a heuristic provides useful inferences by exploiting the structure of information in an environment.

Question 18

Commitment Heuristic

Answer 18

(from the Concepts chapter)
When you commit to a belief when it’s only likely to be true!
Usually because the other outcome is unpleasant, like when you’re speeding and you think there’s a cop car behind you.

Question 19

HALFWAY SUMMARY

Answer 19

First we saw syllogisms and four types of premises.

Then we went on to intuitive statistics, which is a collection of heuristics and biases that we use to understand our world. We saw eight - law of large numbers, law of averages, representativeness, etc etc etc. Illusory correlation as well, and regression to the mean.

Then we went on to Goldstein and Gigerenzer, with their ecological point of view; the recognition heuristic.

Question 20

Regression towards the mean

Answer 20

The phenomenon where if you’re picking random samples out of a population, if your current selection’s at the extreme, your next selection’s more likely to be towards the middle.
This is cuz of the normal distribution.

You’re seeing this now, because people often fall into the illusory correlation trap.

Question 21

The Problem Space

Answer 21

The way that the problem is phrased, including the goal and how you can get to it.

Question 22

Training in Statistics

Answer 22

Expertise in a subject helps you to better detect regression to the mean and statistics. Sometimes they’re domain-specific though.

Question 23

Parsimony

Answer 23

The tendency for subjects to construct the simplest mental model possible.

Question 24

Natural deduction systems

Answer 24

Reasoning using the operators if, then, and, or, not.

Boolean operators!

Question 25

Mental Models

Answer 25

Johnson-Laird! The idea that we create an image in our head as we solve a problem.