Janet means well when she tells her daughter, "Please don’t ever date a boy with a tattoo. People with tattoos are dangerous. A girl down the street dated a boy with a huge tattoo and he beat her up." If Janet knows other people with tattoos who are not dangerous but she holds these beliefs strongly and tells this particular story anyway, she may be engaging in:_______.

Faulty generalization which many times is also referred to as hasty generalization or defective induction – happens when a conclusion for an entire population (that is a group of people e.g Hispanic Women) is based on experience or information collected from limited sample (that is one or two Hispanic women. In other words, we make a faulty generalization when we jump to an unjustified conclusion.

That is, when one makes a hasty generalization, he applies a belief to a larger population than he should based on the information that he has about a few people in that category of people.

Hasty generalization fall into two categories:

1. Obvious

2. Sublime

Examples of Obvious Faulty Generalizations:

1. My friend Bill is bald, so I assume nobody called Bill has hair.

2. If my brother likes to eat a lot of sandwich and soda, and he is healthy, I can say that sandwich and sodas are healthy and don't really make a person fat.

3. You visit a new country and the first person you meet in the airport is rude. You send a message to a friend back home that everyone in this new country is rude.

Example of not so apparent faulty generalization

Our study found that 80% of felines prefer Meow brand cat food.

This sounds impressive, because 80% is a large percentage. But unless we know how many cats were tested, we cannot generalize to the larger feline population. If the study included 10,000 cats, we might have a decent sample size to draw conclusions. But if they only tested five cats, we couldn’t say anything useful about all cats based on what 80% (i.e., four cats) did.

The keys here are:

a) that we should not jump to conclusions about large populations based on a limited sample size.

b) always belive the best of each person regardless of what their background, ethnic or social affiliation or classifiction is.

Cheers!