Chapter 1 Flashcards

1
Q

Deductive reasoning

A

Spelling out whatever conclusion follows logically from your premises, without reference to any external information

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2
Q

Deductive proof

A

Demonstrating that a particular conclusion logically follows from certain premises, and that this conclusion must be true if these premises are true

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3
Q

Truth-preserving

A

when used correctly, deductive reasoning is guaranteed to preserve the truth of its premises is its conclusion.

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4
Q

Unwarranted

A

A conclusion that is not supported by the argument

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5
Q

Necessary condition

A

Must be met if something is to be true, but cannot by itself guarantee the truth of that thing

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6
Q

Sufficient condition

A

One that, if met, does guarantee the truth of something

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7
Q

Logic

A

The study of the principles distinguishing correct from incorrect reasoning

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8
Q

Affirming the antecedent

A

A valid form of argument in which, because one thing is said always to follow from another, the truth of the first guarantees the second is also true

Premise 1: Premise 2: Conclusion:
If A, then B. A. Therefore, B.

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9
Q

Formal fallacy

A

An invalid form of argument representing an error in logic, meaning that arguments in this form cannot be relied on to arrive at valid conclusions

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10
Q

Affirming the consequent

A

An invalid argument which mistakenly assumes that, when one thing always follows from another, the truth of the second also guarantees the first

Premise 1: Premise 2: Conclusion:
If A, then B. B. Therefore, A.

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11
Q

Denying the consequent

A

A valid form of argument in which, because one thing is said always to follow from another, the fact that the second isn’t true also guarantees the first isn’t true

Premise 1: Premise 2: Conclusion:
If A, then B. Not B. Therefore, not A.

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12
Q

Denying the antecedent

A

An invalid argument which mistakenly assumes that, when one thing always follows from another, the fact that the first isn’t true also guarantees the second isn’t true

Premise 1: Premise 2: Conclusion:
If A, then B. Not A. Therefore, not B.

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13
Q

Sound Argument

A

A deductive argument that is both valid and has true premises, meaning its conclusion must also be true

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14
Q

Unsound Argument

A

An argument that does not meet the standard of soundness, either because it is invalid or because one or more of its premises is untrue, or both

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15
Q

Inductive reasoning

A

A form of reasoning in which premises strongly support

a conclusion, but where we can never be absolutely certain that it is true

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16
Q

Ampliative reasoning

A

Another way of describing inductive reasoning – intended to show that such reasoning works by ‘amplifying’ premises into a broader conclusion

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17
Q

Ranking inductive arguments

A

Determining which arguments are more or less convincing relative to one another

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18
Q

Inductive strength or inductive force

A

A measure of how likely we believe an inductive argument is to be true

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19
Q

Cogent

A

An inductive argument that has a good structure, but whose conclusion we should not necessarily accept as true (similarly to a valid deductive argument)

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20
Q

Inductively forceful

A

An inductive argument that has both a good structure and true premises, and whose conclusion we thus have good reason to accept as true (similarly to a sound deductive argument, although without its certainty)

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21
Q

Implicit qualification

A

When a general statement is not literally intended, some implicit qualification needs to be assumed, indicating the frequency with which it applies

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22
Q

Rational expectation

A

Whatever it would be most reasonable to expect in a particular situation; this can be quite different to what somebody personally expects

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23
Q

Sample

A

The particular cases you are using to stand for the entire category about which you wish to make an inductive generalization

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24
Q

N = 1

A

A sample size of one indicates an anecdote rather than a serious investigation; any inductive argument based on a single instance is likely to be very weak

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25
Q

Representative samples

A

Closely resemble the larger group about which claims are being made, while unrepresentative samples fail to do so

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26
Q

Randomized sample

A

One selected at random from across a field of study, with no particular element misleadingly over- represented

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27
Q

Sampling bias

A

Biases introduced by imperfect methods of selecting a sample

28
Q

Observational error

A

Errors due to the accuracy of your measuring system, usually reported as ±X, where X is the potential difference between measured and actual values

29
Q

Margin of error

A

An expression of the degree to which results based on a sample are likely to differ from those of the overall population

30
Q

The problem of induction

A

No matter how likely we believe something to be, an inductive argument can never actually prove it to
be true

31
Q

Falsification

A

The contradiction of something previously accepted as true or obvious

32
Q

Counter-example

A

An example whose discovery makes
it necessary to rethink a particular position, because it directly contradicts a generalization previously believed to be true

33
Q

Black swan event

A

An event that defies both previous experience and expectations based on that experience, making it almost impossible to predict

34
Q

Fallacy

A

A flawed general type of argument that establishes a faulty connection between premises and conclusion, thus failing to give us a good reason to accept the conclusion

35
Q

Fallacious argument

A

An argument whose conclusion does

not follow from its premises, because its reasoning rests on an identifiable fallacy

36
Q

Appeal to popularity

A

a fallacious form of argument based on the assumption that whatever most people think must be true.

37
Q

Appeal to irrelevant authority

A

A fallacious form of argument based on the perceived opinion of an authority without any expertise in a relevant area

38
Q

Unwarranted hidden assumption

A

The faulty, unstated element of reasoning that a fallacy relies on, and that we aim to spell out in order to identify what is at fault

39
Q

Comparable example

A

A method for testing potentially fallacious arguments, and illustrating their flaws, by applying exactly the same reasoning in a different context

40
Q

Informal fallacy

A

If you need to consider both the content of an argument and its relationship with external infor- mation, this is an

41
Q

Formal fallacy

A

If the error is purely related to the structure of an argument, you are dealing with

42
Q

Fallacy of relevance

A

An argument relying on premises that are insufficiently relevant to its conclusion for us to accept this conclusion

43
Q

Argument by appeal

A

The fallacy of appealing to external factors such as authority or popularity to justify a conclusion, rather than using rigorous reasoning

44
Q

Ad hominem

A

The fallacy of attacking the person making

an argument rather than what they actually say

45
Q

Fallacy of ambiguity

A

Shifting the meaning of terms during reasoning, or exploiting uncertainty in order to support an unjustified conclusion

46
Q

Equivocation

A

Using a word in two quite different senses while pretending that they are the same in order to create the appearance of reasoning

47
Q

Amphiboly

A

Using a phrase or sentence that can be interpreted as meaning more than one thing, without clarifying which

48
Q

The fallacy of composition

A

Mistakenly arguing that whatever is true of the individual parts must also be true of the whole

49
Q

The fallacy of division

A

Mistakenly arguing that whatever is true of the whole must also be true of its individual parts

50
Q

Material fallacies

A

Fallacies that either covertly assume the truth of a conclusion or avoid the real issues at stake

51
Q

Begging the question

A

putting the conclusion to be proven into your premises,

thus producing something convincing-sounding that proves nothing

52
Q

Circular reasoning

A

an argument whose premise supports its conclusion, and whose conclusion supports its premise, making it a closed loop

53
Q

Post hoc ergo propter hoc

A

the fallacy of assuming that, when one thing happens after another, the first thing must be the cause of the second thing

54
Q

Correlation is not causation

A

the fallacy of assuming that, if two phenomena or sets of data closely follow one another, one must be caused by the other

55
Q

Inverting cause and effect

A

The fallacy of confusing the direction of causation between two related phenomena, and thus mistakenly labelling an effect as a cause

56
Q

False dilemma

A

Fallaciously claiming that, in a complex situation, it is only possible for one of two things to be true

57
Q

Loaded/complex question

A

Asking a question about one thing that also includes an unstated assumption about another, in an attempt to force someone to accept this assumption

58
Q

Faulty analogy

A

Claiming two things are similar, even though they are not, in order to make an unreasonable conclusion look reasonable

59
Q

Faulty generalization

A

Using a small amount of evidence to justify a much larger observation that isn’t actually warranted

60
Q

Slippery slope

A

Arguing on the basis that, if one small thing is allowed to happen, an inevitable and increasingly serious chain of further events will be set in motion

61
Q

The undistributed middle

A

A formal fallacy which mistakenly confuses something that applies to all members of a category with something that applies only to members of that category

62
Q

Base rate neglect

A

Ignoring the underlying frequency of one element in
an analysis, and
thus potentially reaching an incorrect conclusion about the likelihood of a certain result

63
Q

False negative

A

A negative test result produced in error, when whatever is being tested for is in fact present (e.g. a pregnancy test saying you are not pregnant when you actually are)

64
Q

False positive

A

A positive test result produced in error,
when whatever is being tested for is in fact absent
(e.g. a pregnancy test saying you are pregnant when you actually are not)

65
Q

True positive

A

a positive test result that correctly corresponds to the presence of whatever you’re testing for

66
Q

Bayes’s theorem

A

a method for calculating the probability of an event based on our knowledge of previous events

67
Q

Base rate

A

the initial, underlying likelihood that something we are investigating is the case (e.g. the base rate of disease X in this population is one case per 2,000 people per year)