# Inductive Reasoning by 5zA94m

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• pg 1
```									Inductive Reasoning
Concepts and Principles
of
Construction
Basic Categories
Basic Categories
   Target - the category we are
interested in understanding better
Basic Categories
 Target - the category we are
interested in understanding better
 Sample - the individual or group we
Basic Categories
 Target - the category we are
interested in understanding better
 Sample - the individual or group we
What is known about the sample may be the
result of polling or experimentation.
Basic Categories
 Target - the category we are
interested in understanding better
 Sample - the individual or group we
What is known about the sample may be the
result of polling or experimentation. In polling,
this makes the neutrality and focus of questions a
concern.
Basic Categories
 Target - the category we are
interested in understanding better
 Sample - the individual or group we
What is known about the sample may be the
result of polling or experimentation. In polling,
this makes the neutrality and focus of questions a
concern. In experimentation, the issue is
experimental design.
Basic Categories
 Target - the category we are
interested in understanding better
 Sample - the individual or group we
 Feature in question - the property we
know about in the sample and
Using the basic categories...
Will the governor cut funding for the CSU?

   Target - the new governor’s agenda
(needs to be an identifiable thing)
Using the basic categories...
Will the governor cut funding for the CSU?

 Target - the new governor’s agenda
(needs to be an identifiable thing)
 Sample - whatever we already know
Using the basic categories...
Will the governor cut funding for the CSU?

 Target - the new governor’s agenda
(needs to be an identifiable thing)
 Sample - whatever we already know
 Feature in question - support for
education (notice that the sample’s
features may not correspond
perfectly to those of the target)
Two Main Types of
Inductive Reasoning
   Inductive generalization - intends a
conclusion about a class of things or
events larger than the subset that
serves as the basis for the induction
Two Main Types of
Inductive Reasoning
   Inductive generalization - intends a
conclusion about a class of things or
events larger than the subset that
serves as the basis for the induction

Making this type of argument work often requires
careful collection of facts, including sophisticated
methods of insuring randomness of sample.
Two Main Types of
Inductive Reasoning
 Inductive generalization - intends a
conclusion about a class of things or
events larger than the subset that
serves as the basis for the induction
 Analogical argument - intends a
event, or class relevantly similar to
the sample
   Is the sample representative?
   Is the sample representative?

The more like one another the sample and
target are, the stronger the argument.
   Is the sample representative?

The more like one another the sample and
target are, the stronger the argument.
Paying attention to this concern helps avoid
the biased sample fallacy, which (like all of the
inductive fallacies) results in an unusably weak
induction.
   Is the sample representative?

The more like one another the sample and
target are, the stronger the argument.
Paying attention to this concern helps avoid
the biased sample fallacy, which (like all of the
inductive fallacies) results in an unusably weak
induction. Self-selected samples are known
problems in this regard.
   Is the sample large enough?
   Is the sample large enough?

In general, the larger the sample, the
better.
   Is the sample large enough?

In general, the larger the sample, the
better.

Paying attention to this concern helps avoid
the hasty conclusion and anecdotal evidence
fallacies. These are both very common.
Focus Point: Fallacy of
Anecdotal Evidence
Focus Point: Fallacy of
Anecdotal Evidence
   The sample is small, typically a
single story
Focus Point: Fallacy of
Anecdotal Evidence
 The sample is small, typically a
single story
 The story may be striking
Focus Point: Fallacy of
Anecdotal Evidence
 The sample is small, typically a
single story
 The story may be striking
 The story is treated as though it were
representative of the target
Focus Point: Fallacy of
Anecdotal Evidence
 The sample is small, typically a
single story
 The story may be striking
 The story is treated as though it were
representative of the target
 Best use of the anecdote: to focus
attention (NOT as key premise)
Confidence and Caution
Confidence and Caution
   As sample size grows: either
confidence increases or margin of
error decreases
Confidence and Caution
 As sample size grows: either
confidence increases or margin of
error decreases
 Inductions never attain 100%
confidence or 0% margin of error
Confidence and Caution
 As sample size grows: either
confidence increases or margin of
error decreases
 Inductions never attain 100%
confidence or 0% margin of error
 In many cases, evaluation of these
factors can be reasonable without
being mathematically precise
Mathematical Note:
Law of Large Numbers

While evaluation of factors relevant to the
strength of an induction can be reasonable
without being mathematically precise, in
cases of chance-determined repetitions, more
repetitions will bring alternatives closer to
predictable ratios.

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