.png){kind=icon}
Enumerative Induction
You will be able to
- define enumerative induction and explain how it’s used.
- define target population, sample, and relevant property.
- understand the two ways in which an enumerative induction can fail to be strong.
- understand the error known as hasty generalization and know how to avoid it.
- understand the basics of opinion polls and know the definitions of random sampling, self-selecting sample, margin of error, and confidence level.
Statistical Syllogisms
You will be able to
- explain what a statistical syllogism is.
- define individual, group, characteristic, and proportion.
- understand three ways in which statistical syllogisms can fail to be strong.
Analogical Induction
You will be able to
- formulate and evaluate an argument by analogy.
- use the following criteria to evaluate arguments by analogy: relevant similarities, relevant dissimilarities, the number of instances compared, and diversity among cases.
Causal Arguments
You will be able to
- define causal claims and arguments.
- apply Mill’s methods to the evaluation of causal arguments.
- recognize the ways in which people can make errors in causal reasoning.
- define necessary and sufficient conditions.
- distinguish between necessary and sufficient conditions in everyday contexts.
Mixed Arguments
You will be able to
- explain what a mixed argument is and what its key components are.
- evaluate a mixed argument.