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Inductive Reasoning Concepts and Principles ofConstruction

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Basic Categories

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n Target - the category we are interested in understanding better

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Basic Categories n Target - the category we are interested in understanding better n Sample - the individual or group we already know about or understand

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Basic Categories n Target - the category we are interested in understanding better n Sample - the individual or group we already know about or understand What is known about the sample may be the result of observation, polling or experimentation.

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Basic Categories n Target - the category we are interested in understanding better n Sample - the individual or group we already know about or understand What is known about the sample may be the result of observation, polling or experimentation. Credibility of observation is always an issue. In polling, this makes the neutrality and focus of questions a concern.

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Basic Categories n Target - the category we are interested in understanding better n Sample - the individual or group we already know about or understand What is known about the sample may be the result of observation, polling or experimentation. Credibility of observation is always an issue. In polling, this makes the neutrality and focus of questions a concern. In experimentation, the issue is experimental design.

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Basic Categories n Target - the category we are interested in understanding better n Sample - the individual or group we already know about or understand n Feature in question - the property we know about in the sample and wonder about in the target

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Using the basic categories... Will I have a good future if I stay with Y? n Target - my future with Y (needs to be an identifiable thing)

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Using the basic categories... Will I have a good future if I stay with Y? n Target - my future with Y (needs to be an identifiable thing) n Sample - whatever we already know about Y (favorable and unfavorable)

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Using the basic categories... Will I have a good future if I stay with Y? n Target - my future with Y (needs to be an identifiable thing) n Sample - whatever we already know about Y (favorable and unfavorable) n Feature in question - the goodness of my future (notice that the sample's features may not correspond perfectly to those of the target)

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Two Main Types of Inductive Reasoning n Inductive generalization - intends a conclusion about a class of things or events larger than the subset that serves as the basis for the induction

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Two Main Types of Inductive Reasoning n 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.

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Two Main Types of Inductive Reasoning n Inductive generalization - intends a conclusion about a class of things or events larger than the subset that serves as the basis for the induction Example: Let's say that almost all individuals who have worked out as managers over the past five years belonged to the same religion. Is the best conclusion that people who belong to this religion are good managers?

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Two Main Types of Inductive Reasoning n Inductive generalization - intends a conclusion about a class of things or events larger than the subset that serves as the basis for the induction n Analogical argument - intends a conclusion about a specific thing, event, or class that is relevantly similar to the sample

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Two Main Types of Inductive Reasoning n Analogical argument - intends a conclusion about a specific thing, event, or class that is relevantly similar to the sample Example: I've been able to trust my previous assistants with doing the banking. So I expect I will be able to trust my next assistant the same way.

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Concerns About Samples n Is the sample representative?

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Concerns About Samples n Is the sample representative? The more like one another the sample and target are, the stronger the argument.

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Concerns About Samples n 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.

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Concerns About Samples n 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.

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Concerns About Samples n Is the sample large enough?

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Concerns About Samples n Is the sample large enough? In general, the larger the sample, the better.

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Concerns About Samples n 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.

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Focus Point: Fallacy of Anecdotal Evidence My roommate told me she went to a festival a few weeks ago and got dosed with some drug that totally knocked her out. She woke up on the way to the hospital. Obviously, that festival is something to avoid next year.

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Focus Point: Fallacy of Anecdotal Evidence n The sample is small, typically a single story

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Focus Point: Fallacy of Anecdotal Evidence n The sample is small, typically a single story n The story may be striking

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Focus Point: Fallacy of Anecdotal Evidence n The sample is small, typically a single story n The story may be striking n The story is treated as though it were representative of the target

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Focus Point: Fallacy of Anecdotal Evidence n The sample is small, typically a single story n The story may be striking n The story is treated as though it were representative of the target n Best use of the anecdote: to focus attention (NOT as key premise)

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Confidence and Caution

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n As sample size grows: confidence increases or margin of error decreases

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Confidence and Caution n As sample size grows: confidence increases or margin of error decreases n Inductions never attain 100% confidence or 0% margin of error

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Confidence and Caution n As sample size grows: confidence increases or margin of error decreases n Inductions never attain 100% confidence or 0% margin of error n In many cases, evaluation of these factors can be reasonable without being mathematically precise

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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 can be expected to bring alternatives closer to predictable ratios. It's not a sure thing, but it becomes ever more likely with more repetitions.

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Analogical Reasoning: The Argument from Design Suppose you had never seen a clock and you find one lying on a beach. You’d assume it had been made by an intelligent being. Consider the Earth. It is much more complex than a clock. So it must have been created by an intelligent being. This, says the argument from design, is a good reason to think that a creator God exists. Is it?

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