Monday, April 28, 2008
Preparing for Test #4
This week our test will be about BOTH deductive and inductive argumentation, and focus on the first two types of induction: Inductive Generalizations and Statistical Arguments. This will give you a chance to review deduction (which we did last week) and see what you've been able to learn about induction
So, review everything you did for last week (see below, "Preparing for Test#3") plus
Be able to define:
- strong argument
- weak argument
- cogent argument
- uncogent argument
- Inductive Generalization
- Statistical Argument
- sample population
- double blind
- reference class
Be able to
- distinguish an inductive argument from a deductive argument
- list the inductive argument indicator words
- identify and evaluate inductive arguments from a given text
- tell whether an argument given to you is strong or weak; cogent or uncogent, reliable or unreliable
- show how an inductive argument can be strengthened by adding more evidence (premises).
- Identify and evaluate Inductive Generalizations.
- Identify and evaluate Statistical Arguments.
In understanding inductive generalizations, you should . . .
1) be able to identify the sample population and the population as a whole (i.e. the population that the generalization is about) in an inductive generalization.
2) understand that a good inductive argument should reach a conclusion that is appropriate to the evidence offered in the premises.
3) A more moderate conclusion makes the inference stronger.
4) An overstated conclusion makes the inference weaker.
5) In evaluating inductive generalizations you should ask the following questions:
--Are the premises true?
--Is the sample population large enough?
--Is the sample population representative of the population as a whole?
6) You should understand that a representative sample is similar to the population as a whole in all relevant respects.
In evaluating generalizations that are generated through opinion polls, you should . . .
1) understand that opinion polls operate under the same basic standards as other inductive generalizations insofar as the sample must be large enough and representative of the population as a whole;
2) The size of the sample should be large enough to reach an acceptable margin of error.
3) The sample is best generated randomly (where each member of the population has an equal chance of being selected) so as to avoid bias.
4) recognize the weaknesses in self-selecting samples;
--how the tendency of people to respond to polls dishonestly, and the tendency of agencies with vested interests to ask slanted questions, can bias a poll sample;
--recognize the merits of a double-blind poll for generating objective results.
In evaluating statistical arguments, students should . . .
1) understand the distinction between inductive strength and statistical reliability in statistical arguments;
2) understand how the specificity of the reference class in a statistical argument can impact the strength and reliability of the inference