标题: good stuff for AA (2) [打印本页] 作者: flyingty 时间: 2003-6-26 17:18 标题: good stuff for AA (2) Inductive Fallacies
-------------------------------------------------------------------------------- Inductive reasoning consists of inferring from the properties of a sample to the properties of a population as a whole. For example, suppose we have a barrel containing of 1,000 beans. Some of the beans are black and some of the beans are white. Suppose now we take a sample of 100 beans from the barrel and that 50 of them are white and 50 of them are black. Then we could infer inductively that half the beans in the barrel (that is, 500 of them) are black and half are white.
All inductive reasoning depends on the similarity of the sample and the population. The more similar the same is to the population as a whole, the more reliable will be the inductive inference. On the other hand, if the sample is relevantly dissimilar to the population, then the inductive inference will be unreliable.
No inductive inference is perfect. That means that any inductive inference can sometimes fail. Even though the premises are true, the conclusion might be false. Nonetheless, a good inductive inference gives us a reason to believe that the conclusion is probably true.
The following inductive fallacies are described in this section
1.Hasty Generalization
-------------------------------------------------------------------------------- Definition: The size of the sample is too small to support the conclusion.
Examples: (i) Fred, the Australian, stole my wallet. Thus, all Australians are thieves. (Of course, we shouldn't judge all Australians on the basis of one example.) (ii) I asked six of my friends what they thought of the new spending restraints and they agreed it is a good idea. The new restraints are therefore generally popular.
Proof: Identify the size of the sample and the size of the population, then show that the sample size is too small. Note: a formal proof would require a mathematical calculation. This is the subject of probability theory. For now, you must rely on common sense
2. Unrepresentative Sample
-------------------------------------------------------------------------------- Definition: The sample used in an inductive inference is relevantly different from the population as a whole.
Examples: (i) To see how Canadians will vote in the next election we polled a hundred people in Calgary. This shows conclusively that the Reform Party will sweep the polls. (People in Calgary tend to be more conservative, and hence more likely to vote Reform, than people in the rest of the country.) (ii) The apples on the top of the box look good. The entire box of apples must be good. (Of course, the rotten apples are hidden beneath the surface.)
Proof: Show how the sample is relevantly different from the population as a whole, then show that because the sample is different, the conclusion is probably different
3. False Analogy
-------------------------------------------------------------------------------- Definition: In an analogy, two objects (or events), A and B are shown to be similar. Then it is argued that since A has property P, so also B must have property P. An analogy fails when the two objects, A and B, are different in a way which affects whether they both have property P.
Examples: (i) Employees are like nails. Just as nails must be hit in the head in order to make them work, so must employees. (ii) Government is like business, so just as business must be sensitive primarily to the bottom line, so also must government. (But the objectives of government and business are completely different, so probably they will have to meet different criteria.)
Proof: Identify the two objects or events being compared and the property which both are said to possess. Show that the two objects are different in a way which will affect whether they both have that property.
4. Slothful Induction
-------------------------------------------------------------------------------- Definition: The proper conclusion of an inductive argument is denied despite the evidence to the contrary.
Examples: (i) Hugo has had twelve accidents n the last six months, yet he insists that it is just a coincidence and not his fault. (Inductively, the evidence is overwhelming that it is his fault. This example borrowed from Barker, p. 189) (ii) Poll after poll shows that the N.D.P will win fewer than ten seats in Parliament. Yet the party leader insists that the party is doing much better than the polls suggest. (The N.D.P. in fact got nine seats.)
Proof: About all you can do in such a case is to point to the strength of the inference.
5. Slothful Induction
-------------------------------------------------------------------------------- Definition: The proper conclusion of an inductive argument is denied despite the evidence to the contrary.
Examples: (i) Hugo has had twelve accidents n the last six months, yet he insists that it is just a coincidence and not his fault. (Inductively, the evidence is overwhelming that it is his fault. This example borrowed from Barker, p. 189) (ii) Poll after poll shows that the N.D.P will win fewer than ten seats in Parliament. Yet the party leader insists that the party is doing much better than the polls suggest. (The N.D.P. in fact got nine seats.)
Proof: About all you can do in such a case is to point to the strength of the inference.
6. Fallacy of Exclusion
-------------------------------------------------------------------------------- Definition: Important evidence which would undermine an inductive argument is excluded from consideration. The requirement that all relevant information be included is called the "principle of total evidence".
Examples: (i) Jones is Albertan, and most Albertans vote Tory, so Jones will probably vote Tory. (The information left out is that Jones lives in Edmonton, and that most people in Edmonton vote Liberal or N.D.P.) (ii) The Leafs will probably win this game because they've won nine out of their last ten. (Eight of the Leafs' wins came over last place teams, and today they are playing the first place team.)
Proof: Give the missing evidence and show that it changes the outcome of the inductive argument. Note that it is not sufficient simply to show that not all of the evidence was included; it must be shown that the missing evidence will change the conclusion.作者: anchoret 时间: 2003-6-26 17:28
好东西！！！作者: happyfish0517 时间: 2005-7-14 01:32
UP~