philosophy 11 critical thinking 4

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philosophy 11 critical thinking 4
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  1. The form, or structure, of an argument
    refers to the relationship between an argument’s parts without regard to the argument’s subject matter, or content.

    • P1 All whales are mammals mammals are warm-blooded
    • P2 All intelligent persons study hard
    • C. All whales are warm-blooded

    • example #2
    • P1 All critical thinking students are intelligent

    p2 All critical thinking students study hard

    C. All critical thinking students study hard
  2. Syllogism
    a three-line argument with two premises and one conclusion

    Example – 90% of CSULB freshmen are California residents Susan is a CSULB freshman Hence, Susan is a California resident

    What makes the above argument a statistical syllogism is that the it draws a conclusion about something in particular based on what is generally the case; the premise that is a generalization, the first one, is a statistical generalization.
  3. The form of a statistical syllogism is:
    Any argument having this form is a statistical syllogism, regardless of what the argument is about:

    • 90% of CSULB freshmen are Calif. residents
    • Susan is a CSULB freshman
    • Hence, Susan is a California resident

    • 79% of politicians are honest
    • Smith is a politician
    • Hence, Smith is honest

    Both are statistical syllogisms because they have the same form, in spite of having different subject matter and referring to different percentages.
  4. The form of a statistical syllogism is: pt 2
    x% of F is G a is F a is GThe parts of a statistical syllogism have names:
  5. a = the individual
    • F = the reference class
    • G = the attribute class
  6. inductive arguments 
    There are two factors to be aware of in assessing the strength of a statistical syllogism:

    The closeness to, or remoteness from, 100% of the statistic in the statistical premise – the closer the statistic is to 100%, the greater the confidence you can have in the truth of the conclusion; the farther the statistic is from 100%, the less confidence you can have in the truth of the conclusion.The syllogism’s conformity to the rule of total evidence. This rule states, “In constructing or evaluating statistical syllogisms, be sure that the individual has been placed in the reference class that maximizes the chances of the conclusion being true.” What this rule means is most easily seen by examples of its violation, which is called committing the fallacy of incomplete evidence.
  7. inductive arguments The rule of total evidence: in constructing or evaluating statistical syllogisms, be sure that the individual has been placed in the reference class that maximizes the chances of the conclusion being true. The violation of this rule is called the fallacy of incomplete evidence.
    The rule of total evidence: in constructing or evaluating statistical syllogisms, be sure that the individual has been placed in the reference class that maximizes the chances of the conclusion being true. The violation of this rule is called the fallacy of incomplete evidence.

    Sample #1 – 99% of Fortune 500 companies have male CEO’s Ann Mulcahey is a Fortune 500 company CEO Therefore, Ann Mulcahey is a man

    What has not been taken into account in placing Ann Mulcahey in with the other Fortune 500 company CEO’s?
  8. inductive arguments The rule of total evidence: in constructing or evaluating statistical syllogisms, be sure that the individual has been placed in the reference class that maximizes the chances of the conclusion being true. The violation of this rule is called the fallacy of incomplete evidence.
    • Sample #2 –
    • Most Russians do not speak English The new Russian ambassador to the US is a Russian Therefore, the new Russian ambassador to the US probably does not speak English

    What has not been taken into account in placing the new ambassador in with the other Russians?
  9. Arguments from authority – appeals to what experts have said on some topic instead of presenting direct evidence to support the claims that we make. 
    Example –“I need three fillings in my teeth.” “How do you know?” “Because Dr. Felts, the dentist, told me so.”
  10. Arguments from authority are going to be most persuasive when:
    Arguments from authority are going to be most persuasive when:

    In the area under discussion, there is a consensus, or near consensus, among practitioners about the state of the art; in other words, you are likely to get the same, or a very similar, opinion from one practitioner as from another. When this is not the case, you can have less confidence in the opinion of any single expert.

    In the area under discussion, there is a consensus, or near consensus, among practitioners about the state of the art; in other words, you are likely to get the same, or a very similar, opinion from one practitioner as from another. When this is not the case, you can have less confidence in the opinion of any single expert.
  11. Argument against the person (argumentum ad hominem) – the claim that a statement is probably false because a certain person said it.
    Example –“Arnold Schwarzenegger has no standing to comment on next year’s state budget because of the mess he’s made of his personal life.”  
  12. A sub-variety of ad hominem arguments is most often called by its Latin name: tu quoque (“you too”). The claim that someone’s statement is probably false because he or she is a hypocrite.
    Example –

    Uncle to nephew: “Never let them coffin nails get a hold on you, boy. They’re poison.” Nephew: “Why should I listen to you? You’ve smoked like chimney your whole life!” Nephew’s point is a tu quoque.
  13. argument against the person (ad hominem) circumstantial. This is the claim that someone’s view is probably false not because of who he or she is personally but because of an affiliation or interests that the individual has. 
    Example –

    “Of course the representative from Prince Edward Island opposes the potato surcharge – you know he’s from the biggest producer of potatoes east of Ottawa. He’s bound to be in their pocket! Don’t listen to him.”
  14. Argument from consensus (argumentum ad populum) – the claim that something must be true because most people believe it or false because most people reject it. Look out for this fallacy when anyone begins a sentence with the words, “Well, everyone knows that…” 
    • Example –
    • “You can’t go wrong eating at McDonald’s. Look how many hamburgers they’ve sold!”
  15. Arguments from analogy – the claim that things that are alike in observed ways are probably also alike in unobserved ways.
    Form of an argument from analogy – X has characteristics A, B, C, D, and E Y has characteristics A, B, C, and D Thus, Y has characteristic E
  16. Factors affecting the strength of arguments from analogy 
    Two things compared must be relevantly similar

    An analogy is stronger where there is a greater degree of relevant similarity between the things compared

    The number and variety of instances mentioned in the premises affect the belief-worthiness of the conclusion

    The main point is that the more alike the two things being compared are, the more confident you can be in the truth of the argument from analogy’s conclusion.
  17. The fallacy of false analogy
    occurs when the analogy breaks down in some crucial way, when there are not enough similarities between the two things to warrant the conclusion that there are further, unobserved similarities. As Teays writes, “Similarities make analogies and differences break them."
  18. The fallacy of the slippery slope comes in two versions:
    The claim that since the degree of difference between two things is insignificant, any number of such degrees of is also insignificant.

     The claim that once a process has started, it won’t stop until its ultimate, usually disastrous consequences have occurred; that is, whatever can happen will happen.

    Example (from Douglas Walton’s book Informal Logic) – “Every adult man who is four feet in height is short. If you add one-tenth of an inch to a short man’s height, he…remains short. Therefore, every man is short.” The idea is that if one-tenth of an inch changes nothing with regard to whether a man is short, then one-tenth of an inch is a meaningless difference. So the difference between four feet, one-tenth and four feet, two-tenths is also meaningless since it’s the same amount of difference, and so on.

    What’s fallacious about this kind of reasoning should be easy to see. In the first case, even very small and insignificant increments of difference can add up to a significant difference; in the second case, what might happen is not the same as what must happen.
  19. Inductive generalizations– arguments from the particular to the general; differently, arguments that reach a conclusion about a whole population or group after looking at a sample drawn from that group.
    • Example – P1 58% of the voters polled say they’ll vote for
    • Smith C 58% of all voters will vote for Smith
    • Inductive generalizations are, in a sense, the opposite of statistical syllogisms; there, the argument is from the general to the particular

    Example – P1 Most voters are going to vote for Smith P2 Dolores is a voter C Dolores will (probably) vote for Smith
  20. form of Inductive generalizations which are arguments from the particular to the general
    • Form of an inductive generalization –
    • P1 x% of observed F’s are G’s
    • C x% of all F’s are G’s
  21. Inductive generalizations succeed only if the samples that are the basis of the conclusions are representative, that is, if the features of the population you’re concerned with are adequately reflected in the sample.When is a sample representative? When it is:
    • Adequately large - big enough to overcome any distortions that are possibly present within a small group
    • Adequately varied – contains the same variety as the population
    • Current - reasonably fresh because in some areas of life, things change rapidly
  22. Inductive generalizations go wrong in the following ways:
    • Hasty generalization (aka, jumping to conclusions) – when the sample that is the basis of the generalization is not big enough
    • Biased statistics – when the sample that is the basis of the generalization lacks adequate variety
    • Fallacy of misleading vividness – disregarding the conclusion of a perfectly good inductive generalization in the face of a small amount of conflicting evidenceRelying on an out-of-date sample
  23. Two more fallacies worth knowing about 
    The fallacy of meaningless statistics – when a statistic contains a term so vague that the statistic becomes meaningless because it’s impossible to justify

    • Examples – a) “99% of serious students don’t object to pop quizzes.” (But who, or what, is a “serious student”?) b) “99.8% of real men don’t eat quiche. (But who’s a “real man”?)
    • The fallacy of unknowable statistics – when a statistic is, as a practical matter, impossible to verify

    Example – “In the city of Long Beach, there are 512 blackbirds for every human being.”

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