Test 3 Flashcards
(40 cards)
Implicit Premises and Conclusions
-Some arguments may be said to have “implicit” premises
- Ex: She must be a witch! She flies in the air on a broomstick!
- Without Implicit Premise:
- She flies in the air on a broomstick.
- Therefore, she is a witch.
- A
- Three dots B
- With Implicit Premise:
- She flies in the air on a broomstick.
- [Only witches fly in the air on a broomstick.]
- Therefore, she is a witch.
- A
- [A → B]
- Three dots B
- Without Implicit Premise:
-Some arguments may be said to have “implicit” conclusions
- Ex: The only way I’d be considered for that job is if I already had an undergraduate degree, and I have 2 years left to finish school
- Without Implicit Conclusion:
- The only way I’d be considered for that job is if I already had an undergraduate degree.
- I have 2 years left to finish school.
- A → B
- ~B
- With Implicit Conclusion:
- The only way I’d be considered for that job is if I already had an undergraduate degree.
- I have 2 years left to finish school.
- [Therefore, I won’t be considered for that job.]
- A → B
- ~B
- Three dots ~A
- OR option 2 - The only way I’d be considered for that job is if I already had an undergraduate degree.
- I have 2 years left to finish school.
- [Therefore, I won’t be considered for that job (within the next two years).]
- A → B
- ~B
- Three dots ~A
- Without Implicit Conclusion:
Two Reasons to Add “Implicit” Premises or Conclusions to Your Argument Summaries (e.g. your summary of arguments in “standard form”)
- Clarify what the author intended
- Strengthen the argument
- Whatever implicit premises you add, these should generally follow the principle of charity and the principle of fidelity as much as possible
- Note that in choosing to add implicit premises, you are choosing to follow the principle of charity more than the principle of fidelity in one regard: namely, adding a premise not explicitly stated by the author
Principle of Fidelity
- Your summary of someone else’s argument should stay as close to the author’s intentions (and explicit statements) as possible
- Ex: The woman in the hat is not a witch since witches have long noses and she doesn’t have a longnose.
- Greater “Fidelity” Summary:
- Witches have long noses
- She doesn’t have a long nose.
- Therefore, the woman in the hat is not a witch.
Principle of Charity
- Your summary of someone else’s argument should treat the argument as being as strong an argument as possible
- Ex: The woman in the hat is not a witch since witches have long noses and she doesn’t have a longnose.
- Greater “Charity” Summary
- Witches have long noses.
- The woman in the hat doesn’t have a long nose.
- Therefore, the woman in the hat is not a witch.
Categorical Statements (Evaluating deductive arguments continued)
- Statements that make claims about categories (for instance, about whether some category of things includes or partially overlaps with another category of things)
- Usually includes words like “some,” “all,” or “none”
- Ex:
- All dogs are mammals.
- Fido is a dog.
- Therefore, Fido is a mammal.
- Ex:
- No fish are mammals.
- Sonya is a fish.
- Therefore, Sonya is not a mammal.
-4 Kinds of Categorical Statements
Categorical Inferences/Arguments
-Inferences or arguments that include categorical statements
- Ex:
1. All dogs are mammals.
2. Fido is a dog.
3. Therefore, Fido is a mammal.- Valid
- Ex:
1. No fish are mammals.
2. Sonya is a fish.
3. Therefore, Sonya is not a mammal.- Valid
- Ex:
1. Some egg-layers are mammals.
2. All mammals have lungs.
3. Therefore, some egg-layers have lungs- Valid
- Ex:
1. Some egg-layers are mammals.
2. No mammals are fish.
3. Therefore, some egg-layers are not fish.- Valid
- Ex:
1. Some egg-layers are mammals.
2. Some mammals are dogs.
3. Therefore, some egg-layers are dogs.- Invalid
- Ex:
1. No fish are mammals.
2. All mammals are blind.
3. Therefore, no fish are blind.- Invalid
Four Kinds of Categorical Statements
- Universal Affirmative
- All dogs are mammals
- All S is P
- Universal Affirmative
- Universal Negative
- No fish a mammal.
- No S is P
- Universal Negative
- Particular Affirmative
- Some mammals lay eggs.
- Some S is P
- Particular Affirmative
- Particular Negative
- Some fish don’t lay eggs.
- Some S is not P
- Particular Negative
Evaluating Inductive Inferences
-Focusing on the arrows (where the evaluation of inferences is focused)
How to Evaluate the legitimacy of inferences for Deductive Arguments
- Probably don’t need to know
- An argument where the premises, if true, are intended to prove the conclusion with absolute certainty
- The standard = validity: it’s not possible for the premises to be true and the conclusion false
- Ex: Every dog is a mammal. Fido is a dog. Therefore, Fido is a mammal.
- Validity is an “on-off” (or, pass-fail) notion: Arguments are completely valid, or completely invalid. No argument is “very valid.”
How to Evaluate the legitimacy of inferences for Non-Deductive Arguments
- An argument where the premises, if true, are intended to provide some reason to believe the conclusion, but not establish the conclusion with absolute certainty
- The standard = strength: The premises, if true, would provide strong reason to believe the conclusion is true
- Ex: I’ve seen dozens of swans and every one has been white. Therefore, the next swan I see will probably be white.
- Strength is a “more or less” notion
Some Common Types of Inductive Arguments
-1. Argument from a sample to a statistical generalization
- Inference to the best explanation:
- Argument from (i) some set of data/evidence and (ii) possible explanations of the data/evidence, to (iii) the best among the possible explanations
- Inference to the best explanation:
- Arguments from analogy
- Argument from the similarity of two items in some respects, to their similarity in another respect
- Arguments from analogy
-4. Argument from correlations to a causal claim
Argument from a Sample to a Statistical Generalization
-One common type of inductive arguments
- Includes:
- Population
- Statistical generalization
- Sample
- Two conditions for this argument type
- Ex:
1. 60% of the 5 randomly selected Democrats interviewed in this study answered “yes” to the question “Should Trump be impeached?”
2. Therefore, 60% of registered Democrats support impeaching Trump.
Population
-A set of things, however identified
- Examples of Populations:
- UNLV undergraduates
- Registered Democrats
- All dogs
- All mammals
- The chairs in this room
- Hydrogen atoms in the universe
Statistical Generalization
-A claim that some percentage of a population of things has some characteristics (or characteristics)
- Examples of Statistical Generalizations:
- 55% of registered Democrats support impeaching Trump
- About ¼ of UNLV undergraduates play video games more than 3 hours/week
- More than 80% of the chairs in this room deviate from the manufacturer specifications
Argument from a Sample to a Statistical Generalization (and Sample)
- An argument that concludes to a statistical generalization on the basis of a claim about a sample
- Sample = a subset of a population
- Ex. of arguments from a sample to a statistical generalization
- 55% of the 1000 randomly selected Democrats interviewed in this study answered “yes” to the question “Should Trump be impeached?”
- Therefore, 55% of registered Democrats support impeaching Trump.
- 17 of the 20 chairs in this room that we examined deviated from manufacturer specifications.
- Therefore, more than 80% of the chairs in this room deviate from the manufacturer specifications
Two Conditions for Arguments of This Type (Statistical Generalization) to be strong:
- Adequate sample size
- Unbiased sample
- Thus, also two strategies for developing inference-challenger objections [where applicable]
- Ex:
- 60% of the 5 randomly selected Democrats interviewed in this study answered “yes” to the question “Should Trump be impeached?”
- Therefore, 60% of registered Democrats support impeaching Trump.
- Population = all registered democrats
- Sample = 5 randomly selected democrats
- Problem with sample size
- 17 of the 20 chairs in this room that students have complained about deviated from manufacturer specification.
- Therefore, more than 80% of the chairs in this room deviate from the manufacturer specifications.
- Population = all chairs in this room
- Sample = 20 chairs students have complained about
- Biased sample
Inference to the Best Explanation (IBE)
-One common type of inductive arguments
- Arguments of this type involve the following steps:
- Step 1: Some set of observed facts (or otherwise known or assumed facts)
- Step 2: A proposed explanation of all (or most) of these facts
- Step 3: A comparison between the explanation in Step 2 and other possible explanations that indicated that the Step 2 explanation is “best” (i.e. preferable to the others; superior to the others)
- Step 4: Conclusion that the proposed explanation is true
- Note:
- a. Sometimes these “steps” are presented in a different order
- b. Sometimes each step is represented by multiple premises/conclusions
- c. Sometimes one or more of these steps is “implicit” (not stated)
- Ex:
- The suspect’s fingerprints were found all over the knife that was thrust into the victim’s heart. Furthermore, the suspect was the only person other than the victim that had a key to the victim’s house, and the door was found unlocked. It is natural to conclude that the suspect is guilty of the murder with which he is charged.
-I believe that Napoleon Bonaparte really existed, because the great variety of documents that refer to him would be very hard to explain otherwise.
Seven Virtues of an Explanation
- i.e. Good things for an explanation to have
- Explanation
- Depth
- Power
- Falsifiability
- Modesty
- Simplicity
- Conservativeness
Exploratoriness
-The explanation explains all (or most) of the available data
Depth
-The explanation itself raises no (or few) additional questions
Power
-The explanation applies in many similar situations
Falsifiability
-It is possible that evidence that disconfirms the hypothesis will be found
Modesty
-The explanation involves no (or few) claims about things we don’t know
Simplicity
-The explanation involves fewer factors or events than competing explanations