X
Logical  Fallacy: a error in reasoning
  (adj)     (noun)

(beta)
List Of Fallacies
Play More
Score:
0


About This Game

Feedback Here
Or On Facebook

Statement #143 Discussion

1 comment (1 thead)
All Discussions

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.
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

 742 Total Answer Attempts   58%
 428 Correctly Popped Fallacies
 314 Incorrectly Un/Popped

Most Common Responses

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

Likes for Correct Answers

Show all on page ↑

+
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
0      0

  + Reply 0 comments downstream.



+









Play Game - Fallacy List - Add Statements - Player Collections - Discussions

Login - High Scores - About - Trivium - Links - Contact

Donate To DontFallacy.Me - Support Dr. Labossiere

Creative Commons, 2014, Wiki World Order (Morgan Lesko)


* Fallacious statements are usually paired with a random image of a person who never spoke those words.
This free site is for educational purposes, studying intellectual dishonesty. The images are being used under fair use. Sunflower by robstephaustrali.