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#### Presentation Transcript

**1. **Inductive Reasoning Concepts and Principles
of
Construction

**2. **Basic Categories

**3. **Basic Categories Target - the category we are interested in understanding better

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

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

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

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

**8. **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

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

**10. **Using the basic categories...Will the governor cut funding for the CSU? Target - the governor’s budget agenda (needs to be an identifiable thing)
Sample - whatever we already know about his support for education

**11. **Using the basic categories...Will the governor cut funding for the CSU? Target - the governor’s budget agenda (needs to be an identifiable thing)
Sample - whatever we already know about his support for education
Feature in question - funding for education (notice that the sample's features may not correspond perfectly to those of the target)

**12. **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

**13. **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

**14. **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

**15. **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 that is relevantly similar to the sample

**16. **Two Main Types of Inductive Reasoning Analogical argument - intends a conclusion about a specific thing, event, or class that is relevantly similar to the sample

**17. **Concerns About Samples Is the sample representative?

**18. **Concerns About Samples Is the sample representative?

**19. **Concerns About Samples Is the sample representative?

**20. **Concerns About Samples Is the sample representative?

**21. **Concerns About Samples Is the sample large enough?

**22. **Concerns About Samples Is the sample large enough?

**23. **Concerns About Samples Is the sample large enough?

**24. **Focus Point: Fallacy of Anecdotal Evidence

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

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

**27. **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

**28. **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)

**29. **Confidence and Caution

**30. **Confidence and Caution As sample size grows: confidence increases or margin of error decreases

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

**32. **Confidence and Caution As sample size grows: 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

**33. **Mathematical Note:Law of Large Numbers

**34. **Analogical Reasoning:The Argument from Design