Difference between revisions of "Inductive reasoning falacies"
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| − | Inductive reasoning falacies consist of inferring from the properties of a sample to the properties of a population as a whole. For example: | + | Inductive {{Wiki|reasoning}} falacies consist 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 1,000 marbles. Some of the marbles are black and some of the beans are white. Suppose now we take a sample of 100 marbles from the barrel and that 50 of them are white and 50 of them are black. We could infer inductively that half the marbles in the barrel are black and half are white. All inductive reasoning depends on the similarity of the sample and the population. The more similar the sample is to the population as a whole, the more reliable will be the inductive inference. However, if the sample is relevantly dissimilar to the population, then the inductive inference will be unreliable. No inductive inference is perfect. Even though the premises are true, the conclusion might be false. Nevertheless, a good inductive inference gives us a reason to believe that the conclusion is probably true. | + | Suppose we have a barrel containing 1,000 marbles. Some of the marbles are black and some of the beans are white. Suppose now we take a sample of 100 marbles from the barrel and that 50 of them are white and 50 of them are black. We could infer inductively that half the marbles in the barrel are black and half are white. All inductive {{Wiki|reasoning}} depends on the similarity of the sample and the population. The more similar the sample is to the population as a whole, the more reliable will be the inductive {{Wiki|inference}}. However, if the sample is relevantly dissimilar to the population, then the inductive {{Wiki|inference}} will be unreliable. No inductive {{Wiki|inference}} is perfect. Even though the premises are true, the conclusion might be false. Nevertheless, a good inductive {{Wiki|inference}} gives us a [[reason]] to believe that the conclusion is probably true. |
===Hasty Generalization=== | ===Hasty Generalization=== | ||
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The size of the sample is too small to support the conclusion. For example: | The size of the sample is too small to support the conclusion. For example: | ||
| − | "A taekwondo student was mugged last night. That just proves that taekwondo is useless on the street." | + | "A taekwondo [[student]] was mugged last night. That just proves that taekwondo is useless on the street." |
| − | "I asked a friend if the school has a good instructor. He said "No" so the instructor must not be any good." | + | "I asked a [[friend]] if the school has a good instructor. He said "No" so the instructor must not be any good." |
===Unrepresentative Sample=== | ===Unrepresentative Sample=== | ||
| − | The sample used in an inductive inference is relevantly different from the population as a whole. For example: | + | The sample used in an inductive {{Wiki|inference}} is relevantly different from the population as a whole. For example: |
| − | "To find out if taekwondo is any good, we asked people in a martial arts online forum." | + | "To find out if taekwondo is any good, we asked [[people]] in a [[martial arts]] online forum." |
===False Analogy=== | ===False Analogy=== | ||
| − | 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. For example: | + | 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. For example: |
| − | "Students are like nails. Just as nails must be hit in the head in order to make them work, so must students." | + | "Students are like {{Wiki|nails}}. Just as {{Wiki|nails}} must be hit in the head in order to make them work, so must students." |
===Slothful Induction=== | ===Slothful Induction=== | ||
| − | The proper conclusion of an inductive argument is denied despite the evidence to the contrary. For example: | + | The proper conclusion of an inductive argument is denied despite the {{Wiki|evidence}} to the contrary. For example: |
"Frank says he was robbed of the match at the tournament, even though the punched the opponent in the face three separate times." | "Frank says he was robbed of the match at the tournament, even though the punched the opponent in the face three separate times." | ||
| − | ===Fallacy of Exclusion=== | + | ===[[Fallacy]] of Exclusion=== |
| − | 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." For example: | + | Important {{Wiki|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 {{Wiki|evidence}}." For example: |
| − | "A taekwondo student got beat in a street fight last night." (The information left out is that he was attacked by four opponents." | + | "A taekwondo [[student]] got beat in a street fight last night." (The [[information]] left out is that he was attacked by four opponents." |
{{R}} | {{R}} | ||
[http://tkdtutor.com/articles/topics/martial-arts/219-fallacies/1315-inductive tkdtutor.com] | [http://tkdtutor.com/articles/topics/martial-arts/219-fallacies/1315-inductive tkdtutor.com] | ||
[[Category:Martial arts]] | [[Category:Martial arts]] | ||
Revision as of 11:28, 30 May 2014
Inductive reasoning falacies consist 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 1,000 marbles. Some of the marbles are black and some of the beans are white. Suppose now we take a sample of 100 marbles from the barrel and that 50 of them are white and 50 of them are black. We could infer inductively that half the marbles in the barrel are black and half are white. All inductive reasoning depends on the similarity of the sample and the population. The more similar the sample is to the population as a whole, the more reliable will be the inductive inference. However, if the sample is relevantly dissimilar to the population, then the inductive inference will be unreliable. No inductive inference is perfect. Even though the premises are true, the conclusion might be false. Nevertheless, a good inductive inference gives us a reason to believe that the conclusion is probably true.
Hasty Generalization
The size of the sample is too small to support the conclusion. For example:
"A taekwondo student was mugged last night. That just proves that taekwondo is useless on the street."
"I asked a friend if the school has a good instructor. He said "No" so the instructor must not be any good."
Unrepresentative Sample
The sample used in an inductive inference is relevantly different from the population as a whole. For example:
"To find out if taekwondo is any good, we asked people in a martial arts online forum."
False Analogy
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. For example:
"Students are like nails. Just as nails must be hit in the head in order to make them work, so must students."
Slothful Induction
The proper conclusion of an inductive argument is denied despite the evidence to the contrary. For example:
"Frank says he was robbed of the match at the tournament, even though the punched the opponent in the face three separate times."
Fallacy of Exclusion
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." For example:
"A taekwondo student got beat in a street fight last night." (The information left out is that he was attacked by four opponents."
