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Notice how often you use inductive reasoning throughout your day. At home, work, or school, as you
travel from place to place, what conclusions do you draw from what you see around you?
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Read a detective story or watch a detective show like Without a Trace, NYPD Blue, or Law & Order. Pay
special attention to how detectives use evidence to draw conclusions about the crime.
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L E S S O N
Jumping to
Conclusions
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LESSON SUMMARY
Just as there are logical fallacies to beware of in deductive reasoning,
there are several logical fallacies to look out for in inductive reasoning.
This lesson will show you how to recognize and avoid those fallacies.
magine a coworker of yours, Dennis, bumps into you during a coffee break. You know, I tried the coffee
at the new deli this morning, he says, and it was lousy. What a shame, the new deli stinks.
I
Oops. Dennis has just been caught jumping to conclusions.
Inductive reasoning, as you know, is all about drawing conclusions from evidence. But sometimes, people
draw conclusions that aren t quite logical. That is, conclusions are drawn too quickly or are based on the wrong
kind of evidence. This lesson will introduce you to the three logical fallacies that lead to illogical conclusions in
inductive reasoning: hasty generalizations, biased generalizations, and non sequiturs.
Hasty Generalizations
A hasty generalization is a conclusion that is based on too little evidence. Dennis s conclusion about the new deli
is a perfect example. He d only been to the new deli once, and he d only tried one item. Has he given the deli a
fair chance? No. First of all, he s only tried the coffee, and he s only tried it one time. He needs to have the coffee
a few more times before he can fairly determine whether or not their coffee is any good. Second, he needs to try
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JUMPING TO CONCLUSIONS
Practice
their other foods as well before he can pass judgment
Are any of the following hasty generalizations?
on the whole establishment. Only after he has collected
this evidence will he have enough premises to lead to
a logical conclusion. 1. The new quarterback threw two interceptions
and only completed two passes in the first game.
Here s another example of a hasty generalization.
Looks like we re in for a losing season.
Let s say you re introduced to a woman named Ellen at
work, and she barely acknowledges you. You decide
2. The last five times I saw Edna, she was with
she s cold and arrogant. Is your conclusion fair? Maybe
Vincent. They must be going out.
Ellen was preoccupied. Maybe she was sick. Maybe she
had a big meeting she was heading to. Who knows? The
point is, you only met her once, and you drew a con- 3. That s twice now I ve had to wait for the bus
because it was late. I guess buses are never on
clusion about her based on too little evidence.
time around here.
A few weeks later, you meet Ellen again. This
time, she s friendly. She remembers meeting you, and
Answers
you have a pleasant conversation. Suddenly you have to
1. Yes, this is a hasty generalization. It s only the first
revise your conclusion about her, don t you? Now you
game, and the quarterback is new. Give him a
think she s nice. But the next time you see her, she
chance to warm up!
doesn t even say hello. What s happening here? You
2. Since you ve seen them together five times, there s
keep jumping to conclusions about Ellen. But you really
a pretty strong likelihood that Edna and Vincent
need to have a sufficient number of encounters with her
are involved in some kind of relationship, so this
before you can come to any conclusions.
is not a hasty generalization.
Hasty generalizations have a lot in common with
3. This is a hasty generalization. It could be you ve
stereotypes. In the case of stereotypes, conclusions
just had bad luck the two times you wanted to
about an entire group are drawn based upon a small
ride the bus. You need to try the bus a few more
segment of that group. Likewise, hasty generalizations
draw conclusions about something based on too small times before you can comfortably conclude that
a sample, such as one cup of coffee, or two or three the buses are always late.
encounters with Ellen.
Here are a few more hasty generalizations:
Biased Generalizations
Brandon is a jock, and he s a lousy student. All jocks
are lousy students. On a local TV program, you hear that a recent poll
shows that 85 percent of people surveyed support
Suzie is blonde, and she has a lot of fun. So I guess drilling for oil in Alaska s Arctic National Wildlife
it s true that blondes have more fun. Refuge. If most Americans feel this way, you think that
maybe you should rethink your position on the issue.
You d need to see a lot more examples of jocks and Unfortunately, what you haven t been told is that the
blondes before either of these conclusions could be only people who were surveyed for this poll were
justified. employees of major oil companies.
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JUMPING TO CONCLUSIONS
The problem with a survey like this (there will be If this conclusion is based on evidence from
more on surveys in Lesson 18, Numbers Never Lie ) biased sources, then the generalization (the conclu-
is that the pool of people it surveyed was biased. Think sion) is biased. For example, if those friends who say
about it for a moment. Employees of oil companies are that fraternities are a waste of time are also friends
going to favor drilling for oil because it will generate who had wanted to be in a fraternity but had not been
revenue for the oil companies, which in turn means job invited to join, then they re likely to have a negative
security for the employees. Therefore, the conclusion (biased) opinion of fraternities. Hence, their conclusion
that the majority of Americans favor drilling for oil in would be biased.
Alaska s Arctic National Wildlife Refuge is biased as On the other hand, how could this be a reliable
well. It s based on a survey of biased respondents and, inductive argument? Write your answer below.
as a result, cannot be considered representative of
Americans as a whole.
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