1. Evaluate Inductive Reasoning and Spot Inductive Fallacies
Chapter 9
Evaluate Inductive Reasoning and Spot Inductive Fallacies

2. Inductions and the Evidence at Hand
Evaluating Generalizations
A generalization may be based on data gathered systematically or unsystematically
The premises report personal experiences, conversations focused on these topics:
Or information derived from historical records or opinion surveys
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3. Inductions and the Evidence at Hand
People over the age of 60 tend to prefer to listen to oldies
73 percent of the hotel room beds in this city are infested with bedbugs
It’s probably going to be cloudy and cold in San Francisco if you go in August
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4. Inductions and the Evidence at Hand
It is easy to imagine scenarios in which the information in the premises is true
But the conclusion may not apply
To evaluate the logical strength of inductive generalizations:
We need to do more than find a single counterexample
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5. Inductions and the Evidence at Hand
Asking four questions and finding satisfactory answers to each of them
Was the Correct Group Sampled?
Were the Data Obtained in an Effective Way?
Were Enough Cases Considered?
Was the Sample Representatively Structured?
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6. Inductions and the Evidence at Hand
Coincidences, Correlations, and Causes
The progression from coincidence to correlation to causal explanations marks our:
Progress in being able to explain and to predict events
As more data are systematically gathered and analyzed:
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7. Inductions and the Evidence at Hand
We may discover that the two events are statistically correlated
Which Report Should We Believe and Why? The Devil Is in the details!
Coincidences
We can use inductive reasoning and statistical facts to calculate the probabilities that:
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8. Inductions and the Evidence at Hand
A given coincidence might occur
Correlations
Describe the degree to which two different sets of events are aligned
Well-researched correlations can be powerful tools
Causes
Causal explanations are desirable because they:
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9. Inductions and the Evidence at Hand
Enable us to explain, predict, and control parts of the natural world
It is not always possible to move all the way:
From coincidence to correlation to causal explanation in every field of inquiry
Fallacies Masquerading as Strong Inductive Arguments
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10. Inductions and the Evidence at Hand
Erroneous Generalization
At times, we generalize from too little information:
Exaggerating the claim beyond what the data support
Playing with Numbers
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11. Inductions and the Evidence at Hand
False Dilemma
A real dilemma is a situation in which all our choices are bad
The Gambler’s Fallacy
Random events, by definition, are not causally connected
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12. Inductions and the Evidence at Hand
False Cause
When two events occur one right after the other:
We may mistakenly infer that the first is the cause of the second
Other mistakes often grouped under the:
Broad heading of False Cause Fallacies result from confusing symptoms
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13. Inductions and the Evidence at Hand
Slippery Slope
Everyone knows that simply beginning something is no assurance that it will be completed
The Slippery Slope Fallacy makes the false assumption that discrete events are linked together
The first step in the process necessarily results in:
Some significant, usually bad, result way down the road
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