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58 Cards in this Set
- Front
- Back
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Deductive argument
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is intended to provide logically conclusive support for its conclusion, being valid or invalid, sound or unsound.
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Inductive argument
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is intended to supply only probable support for its conclusion, earning the label of "strong" if it succeeds in providing such support and "weak" if it fails.
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Enumerative induction
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When we being with observations about some members of the group and end with a generalization about all of them.
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Target population or Target group
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the group as a whole-the whole collection of individuals in questions
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Sample members or sample
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the observed members of the target group
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Relevant property or property in question
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the property we're interested in
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If the sample is not representative of the target group, we can:
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we can fault the argument on another count
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If the sample is not representative of the whole:
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we cannot use it to draw accurate conclusions about the whole.
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An enumerative inductive argument can fail to be strong in two major ways: Its sample can be:
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1. too small
2. not representative |
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It is possible for an enumerative induction to be perfectly strong but have _____ premises.
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false
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Hasty Generalization
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The fallacy of drawing a conclusion about a target group based on an inadequate sample size.
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The larger the sample size,
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the more likely it is to reliably reflect the nature of the larger group
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The more homogeneous a target group is in traits relevant to the property in question_____________.
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the smaller the sample can be.
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The less homogeneous a target group is in traits relevant to the property in question_______________.
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the larger the sample should be
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Representative sample
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a sample that must resemble the target group in all the ways that matter.
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Biased sample
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a sample that does not properly represent the target group.
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When is an enumerative inductive argument strong?
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Only if the sample is representative of the whole.
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To be truly representative, the sample must be like the target group by:
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1. having all the same relevant characteristics
2. having them in the same proportions that the target group does |
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"relevant characteristics"
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features that could influence the property in question
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Selective attention
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the tendency to observe and remember things that reinforce our beliefs and to gloss over and dismiss things that undercut those beliefs.
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Opinion polls should:
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1. be strong
2. have true premises |
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Any opinion poll worth believing must:
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1. use a large enough sample that accurately represents the target population in all the relevant features
2. generate accurate data (the results must correctly reflect what they purport to be about |
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In national polling, what sample sizes are needed?
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samples need not be enormous to be accurate reflections of the larger target population.
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Why is random sampling used?
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to ensure that a sample is truly representative of the target group and the sample must be selected randomly from the target group.
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Simple random selection
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every member of the target group has an equal chance of being selected for the sample.
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For a random sample, researchers and pollsters may:
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assign a number to each member of a population, then use a random-number generator to make the selections.
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Self-selecting sample
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a type of ample that usually tells you very little about the target population
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How do magazines, newpapers, talk shows, and news programs sometimes acknowledge the use of self-selecting samples?
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by labeling the survey in question as "unscientific"
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But the media frequently tout the results of such distorted surveys as though:
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the numbers actually mean something
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Margin of error
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the variation between the values derived from a sample an the true value of the whole target group.
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confidence level
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the probability that the sample will accurately represent the target group within the margin f error.
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How can polling errors such as bias occur?
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because of poorly worked questions or researchers who may consciously or unconsciously influence the kinds of answers received.
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How can sample size, margin of error and confidence level related?
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1. the larger the sample, the smaller the margin of error
2. the lower the confidence level, the smaller the sample size can be 3. the larger the margin of error, the higher the confidence level can be |
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An enumerative induction:
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must be strong and have true premises for us to be justified in accepting the conclustion
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Analogy
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a comparison of two or more things alike in specific respects
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An analogy can be used to:
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argue inductively for a conclustion (argument by analogy)
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An analogical induction reasons because:
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two or more things are similar in several respects, they must be similar in some further respect
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The greater the degree of similarity between the two things being compared:
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the more probable the conclusion is
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To use an analogy to support a particular conclusion:
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all you have to do is find two things with some similarities and then reason that the two things are similar in yet another way
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There are some criteria we can use to judge the strength of arguments by analogy:
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1. relevant similarities
2. relevant dissimilariteis 3. the number of instances compared 4. diversity among cases |
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A similarity(or dissimilarity) is relevant to an argument by analogy:
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if it has an effect on whether the conclusion is probably true.
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The more relevant dissimilarities:
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the less probable the conclusion
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The greater the number of instances that show the relevant similarities:
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the stronger the argument
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Casual claim
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a statement about the causes of things
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Causal argument
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an inductive argument whose conclusion contains a causal claim
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Casual arguments can come in several inductive forms:
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1. We reason about cause and effect using enumerative induction
2. we may argue to a causal conclusion using analogical induction 3. we reason to a causal conclusion by pinpointing the best explanation for a particular effect |
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Inference to the best explanation
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the essence of scientific thinking and a mainstay of our everyday problem-solving and knowledge acquisition
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Several ways of evaluating causal arguments and formulated them into what are now known as:
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"Mill's method" of inductive inference
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What does the Method of Agreement say?
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that if two or more occurrences of a phenomenon have only on relevant factor in common, that factor must be the cause.
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What does the Method of Difference say?
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that the relevant factor present when a phenomenon occurs, and absent when the phenomenon does not occur, must be the cause
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If we combine these two reasoning patterns, we get a modified version of what Mill called the
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Joint Method of Agreement and Difference
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This combined method says that the likely cause is the one isolated when you:
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1. identify the relevant factors common to occurrences of the phenomenon
2. discard any of these that are present even when there are no occurrences |
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Method of Concomitant
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the method says that when two events are correlated- when one varies in close connection with the other-they are probably causally related
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Dose-response relationship
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the higher the dose of the element in question, the higher the response
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How do you distinguish coincidence from cause and effect?
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Don't assume that a causal connection exists unless you have good reason for doing so
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post hoc, ergo propter hoc
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("after that, therefore because of that") We believe that a cause must precede its effect
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Necessary condition
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the occurrence of an event is one without which the event cannot occur
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Sufficient conditions
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the occurrence of an event is one that guarantees that the event occurs
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