Inductive Reasoning by 5zA94m

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									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
  already know about or understand
        Basic Categories
 Target - the category we are
  interested in understanding better
 Sample - the individual or group we
  already know about or understand
    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
  already know about or understand
    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
  already know about or understand
    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
  already know about or understand
 Feature in question - the property we
  know about in the sample and
  wonder about in the target
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
    about his ideas about education
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
    about his ideas about education
   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
  conclusion about a specific thing,
  event, or class relevantly similar to
  the sample
Concerns About Samples
   Is the sample representative?
Concerns About Samples
   Is the sample representative?

    The more like one another the sample and
    target are, the stronger the argument.
Concerns About Samples
   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.
Concerns About Samples
   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.
Concerns About Samples
   Is the sample large enough?
Concerns About Samples
   Is the sample large enough?

    In general, the larger the sample, the
    better.
Concerns About Samples
   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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