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Statement #143 Discussions - Don't Fallacy Me
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Logical  Fallacy: a error in reasoning
  (adj)     (noun)

(beta)

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List Of Fallacies
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Statement #143 Discussion

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Below is the statement as it appears with the fallacy marked as correct. You can see the totals of most frequent responses to this statement. And after reading the any discussion going on below, you can select your choice(s) for the correct answer. For now, whoever posts each statement can update corrections.
People who own wide screen televisions are not poor and should not be allowed to collect welfare or medicaid benefits.

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Hasty Generalization
AKA Fallacy of Insufficient Statistics, Fallacy of Insufficient Sample, Leaping to A Conclusion, Hasty Induction

Category: Fallacies of Presumption

This fallacy is committed when a person draws a conclusion about a population based on a sample that is not large enough. It has the following form:

  1. Sample S, which is too small, is taken from population P.
  2. Conclusion C is drawn about Population P based on S.
The person committing the fallacy is misusing the following type of reasoning, which is known variously as Inductive Generalization, Generalization, and Statistical Generalization:
  1. X% of all observed A's are B's.
  2. Therefore X% of all A's are B's.
The fallacy is committed when not enough A's are observed to warrant the conclusion. If enough A's are observed then the reasoning is not fallacious.

Small samples will tend to be unrepresentative. As a blatant case, asking one person what she thinks about gun control would clearly not provide an adequate sized sample for determining what Canadians in general think about the issue. The general idea is that small samples are less likely to contain numbers proportional to the whole population. For example, if a bucket contains blue, red, green and orange marbles, then a sample of three marbles cannot possible be representative of the whole population of marbles. As the sample size of marbles increases the more likely it becomes that marbles of each color will be selected in proportion to their numbers in the whole population. The same holds true for things others than marbles, such as people and their political views.

Since Hasty Generalization is committed when the sample (the observed instances) is too small, it is important to have samples that are large enough when making a generalization. The most reliable way to do this is to take as large a sample as is practical. There are no fixed numbers as to what counts as being large enough. If the population in question is not very diverse (a population of cloned mice, for example) then a very small sample would suffice. If the population is very diverse (people, for example) then a fairly large sample would be needed. The size of the sample also depends on the size of the population. Obviously, a very small population will not support a huge sample. Finally, the required size will depend on the purpose of the sample. If Bill wants to know what Joe and Jane think about gun control, then a sample consisting of Bill and Jane would (obviously) be large enough. If Bill wants to know what most Australians think about gun control, then a sample consisting of Bill and Jane would be far too small.

People often commit Hasty Generalizations because of bias or prejudice. For example, someone who is a sexist might conclude that all women are unfit to fly jet fighters because one woman crashed one. People also commonly commit Hasty Generalizations because of laziness or sloppiness. It is very easy to simply leap to a conclusion and much harder to gather an adequate sample and draw a justified conclusion. Thus, avoiding this fallacy requires minimizing the influence of bias and taking care to select a sample that is large enough.

One final point: a Hasty Generalization, like any fallacy, might have a true conclusion. However, as long as the reasoning is fallacious there is no reason to accept the conclusion based on that reasoning.

Click For Fallacy Description

 952 Total Answer Attempts   58%
 550 Correctly Popped Fallacies
 402 Incorrectly Un/Popped

Most Common Responses

 
550 - Hasty Generalization
56 - Biased Generalization
23 - Misleading Vividness
21 - Circumstantial Ad Hominem
19 - Fallacy of Division
18 - Red Herring
18 - Fallacy of Composition
17 - Poisoning the Well
17 - Confusing Cause and Effect
16 - Appeal to Spite
16 - Guilt by Association
16 - Genetic Fallacy
15 - Relativist Fallacy
13 - False Dilemma
11 - Begging the Question
11 - Appeal to the Consequences of a Belief
10 - Ad Hominem
10 - Appeal to Pity
10 - Appeal to Novelty
9 - Burden of Proof
8 - Slippery Slope
8 - Personal Attack
8 - Ignoring a Common Cause
8 - Appeal to Common Practice
6 - Appeal to Ridicule
6 - Appeal to Belief
6 - Post Hoc
4 - Appeal to Emotion
4 - Ad Hominem Tu Quoque
4 - Appeal to Popularity
4 - Appeal to Tradition
4 - Appeal to Authority
3 - Special Pleading
3 - Gambler's Fallacy

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The fallacy is not Hasty Gen.
Hasty Gen. form is: Sample S is taken from population P. Sample S is a very small part of population P. Conclusion C is drawn from sample S and applied to population P. In this meme, it is not saying that all poor people own Flat screen TVs Or that all poor people should be cut social benefits because of a sample S amongst population P. What is fallacious in this example is the assumption that being part of Sample S makes you non-P. It begs the question (petitio principii) : How are they disqualified as P for being part of sample S?

10.2.19 06:46 by StephaneBlouin
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