The document discusses inductive arguments, distinguishing between strong and weak examples, particularly in the context of generalizations made from sample populations. It explores statistical arguments, emphasizing that broader conclusions and higher percentages increase reliability, while specifics about the reference class enhance argument strength. Additionally, the document explains arguments from analogy, providing examples to illustrate how similarities can suggest further characteristics.

2. Introduction
• Inductive Argument: an argument
in which the premises are intended
to provide support, but not
conclusive evidence, for the
conclusion.
• Strong Inductive Argument: an
inductive argument in which the
premises actually do make the
conclusion more likely to be true
(rather than false).
– Remember, strength comes in
degrees

3. Inductive
Generalizations
• Generalization: statement made about all
or most members of a group.
• Inductive generalization: inductive
argument that relies on characteristics
of a sample population (i.e., a portion of
the population) to make a claim about
the population as a whole.
– i.e., an inductive argument with a
generalization as a conclusion.
• Example: All the bass Hank caught in
the Susquehanna have been less than
1lb. So, most of the bass in the
Susquehanna are less than 1lb.

4. • Notice…
– All the bass Hank caught in the Susquehanna
have been less than 1lb. So, all of the bass in
the Susquehanna are less than 1lb.
• ..is a pretty weak argument. Even if Hank
fishes often, the Susquehanna is a big
river and his catches are not enough to
justify such a “sweeping conclusion.”
• However, if we changed the conclusion
to “most of the bass are…” or, better yet,
“many of the bass are…” the argument
would be much stronger.

5. • Statistical arguments go “the other way.” They take
generalizations and draw conclusions about smaller
samples of the population (usually individuals).
• Example:
1. You’re a college student
2. 90% of college students want no cumulative final.
3. So you probably don’t want a cumulative final.
• The more “broad” the conclusion the better.
• The higher the original percentage, the better.
• Usually, if the percentage is around 50%, we will call
the argument unreliable, even if it is more likely
than not that the conclusion is true.
– A “rule of thumb”: if it is would reasonable to bet on
it, then it is reliable.

6. • The reference class is the group to
which statistics apply.
• As a rule, the more specific the
reference class is, the better the
argument is.
• A statistical argument can be used
to support a conclusion about a
group rather than an individual.
– 90% of college students are in favor of
not having a final exam.
– So, 90% of 21 students are in favor of
not having a final exam.

7. • In a statistical argument, if you
find out more information about
the person in question, you
“narrow” the group (class) the
person is in.
• Example:
1. You are a college student who likes
essays.
2. 85% of college students who like
essays want cumulative fails.
3. Thus you probably want a cumulative
final.
• This additional information
weakened our justification for
believing that you don’t want a
final.

8. • Analogy: comparison of things
based on similarities.
• Argument from analogy: an
argument that suggests that the
presence of certain similarities is
evidence for further similarities.
• Common Form:
1. A and B have characteristic X
2. A has characteristic Y
3. So B probably has characteristic Y
too.
• Example:
1. Tiffany and Heather are both tall and
play basketball.
2. Tiffany also plays volleyball.
3. So, Heather probably plays volleyball
too.

9. – Squirrels and rats are rodents of
similar size and appearance.
– Rats cause problems in the city,
and squirrels cause problems in
the suburbs.
– Rats should be exterminated.
– So, squirrels should be
exterminated.
– Is this a good argument?

10. • Tiffany, Heather, Amber and Krissy
are all tall and play basketball.
• Tiffany, Amber and Krissy also play
volleyball.
• So, Heather must also play
volleyball.
• Sample size strengthens an
argument

11. Jimber Atienza