Easy - Medium - Hard
Changing difficulty modes will reset your session score.
Check the About page for difficulty details.

Play The Game, Reclaim Your Brain

Select the one clearest logical fallacy in the example,
then click the POP bubble below. New here?.. Check out the Fallacy List first.
Jane: "Did you hear about those terrorists killing those poor people? That sort of killing is just wrong."
Sue: "Those terrorists are justified. After all, their land was taken from them. It is morally right for them to do what they do."
Jane: "Even when they blow up busloads of children?"
Sue: "Yes.
Disagree with 'correct' answer?
Join the discussion.
[in new window]
Biased Generalization
AKA Biased Statistics, Loaded Sample, Prejudiced Statistics, Prejudiced Sample, Loaded Statistics, Biased Induction

Category: Fallacies of Presumption

This fallacy is committed when a person draws a conclusion about a population based on a sample that is biased or prejudiced in some manner. It has the following form:

  1. Sample S, which is biased, 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 the sample of A's is likely to be biased in some manner. A sample is biased or loaded when the method used to take the sample is likely to result in a sample that does not adequately represent the population from which it is drawn.

Biased samples are generally not very reliable. As a blatant case, imagine that a person is taking a sample from a truckload of small colored balls, some of which are metal and some of which are plastic. If he used a magnet to select his sample, then his sample would include a disproportionate number of metal balls (after all, the sample will probably be made up entirely of the metal balls). In this case, any conclusions he might draw about the whole population of balls would be unreliable since he would have few or no plastic balls in the sample.

The general idea is that biased samples are less likely to contain numbers proportional to the whole population. For example, if a person wants to find out what most Americans thought about gun control, a poll taken at an NRA meeting would be a biased sample.

Since the Biased Sample fallacy is committed when the sample (the observed instances) is biased or loaded, it is important to have samples that are not biased making a generalization. The best way to do this is to take samples in ways that avoid bias. There are, in general, three types of samples that are aimed at avoiding bias. The general idea is that these methods (when used properly) will result in a sample that matches the whole population fairly closely. The three types of samples are as follows...

Random Sample: This is a sample that is taken in such a way that nothing but chance determines which members of the population are selected for the sample. Ideally, any individual member of the population has the same chance as being selected as any other. This type of sample avoids being biased because a biased sample is one that is taken in such a way that some members of the population have a significantly greater chance of being selected for the sample than other members. Unfortunately, creating an ideal random sample is often very difficult.

Stratified Sample: This is a sample that is taken by using the following steps: 1) The relevant strata (population subgroups) are identified, 2) The number of members in each stratum is determined and 3) A random sample is taken from each stratum in exact proportion to its size. This method is obviously most useful when dealing with stratified populations. For example, a person's income often influences how she votes, so when conducting a presidential poll it would be a good idea to take a stratified sample using economic classes as the basis for determining the strata. This method avoids loaded samples by (ideally) ensuring that each stratum of the population is adequately represented.

Time Lapse Sample: This type of sample is taken by taking a stratified or random sample and then taking at least one more sample with a significant lapse of time between them. After the two samples are taken, they can be compared for changes. This method of sample taking is very important when making predictions. A prediction based on only one sample is likely to be a Hasty Generalization (because the sample is likely to be too small to cover past, present and future populations) or a Biased Sample (because the sample will only include instances from one time period).

People often commit Biased Sample because of bias or prejudice. For example, a person might intentionally or unintentionally seek out people or events that support his bias. As an example, a person who is pushing a particular scientific theory might tend to gather samples that are biased in favor of that theory.

People also commonly commit this fallacy because of laziness or sloppiness. It is very easy to simply take a sample from what happens to be easily available rather than taking the time and effort to generate an adequate sample and draw a justified conclusion.

It is important to keep in mind that bias is relative to the purpose of the sample. For example, if Bill wanted to know what NRA members thought about a gun control law, then taking a sample at a NRA meeting would not be biased. However, if Bill wanted to determine what Americans in general thought about the law, then a sample taken at an NRA meeting would be biased.

Click For Fallacy Description
16
Ignoring a Common Cause
AKA Questionable Cause

Category: Fallacies of Presumption → Casual Fallacies

This fallacy has the following general structure:

  1. A and B are regularly connected (but no third, common cause is looked for).
  2. Therefore A is the cause of B.
This fallacy is committed when it is concluded that one thing causes another simply because they are regularly associated. More formally, this fallacy is committed when it is concluded that A is the cause of B simply because A and B are regularly connected. Further, the causal conclusion is drawn without considering the possibility that a third factor might be the cause of both A and B.

In many cases, the fallacy is quite evident. For example, if a person claimed that a person's sneezing was caused by her watery eyes and he simply ignored the fact that the woman was standing in a hay field, he would have fallen prey to the fallacy of ignoring a common cause. In this case, it would be reasonable to conclude that the woman's sneezing and watering eyes was caused by an allergic reaction of some kind. In other cases, it is not as evident that the fallacy is being committed. For example, a doctor might find a large amount of bacteria in one of her patients and conclude that the bacteria are the cause of the patient's illness. However, it might turn out that the bacteria are actually harmless and that a virus is weakening the person, Thus, the viruses would be the actual cause of the illness and growth of the bacteria (the viruses would weaken the ability of the person's body to resist the growth of the bacteria).

As noted in the discussion of other causal fallacies, causality is a rather difficult matter. However, it is possible to avoid this fallacy by taking due care. In the case of Ignoring a Common Cause, the key to avoiding this fallacy is to be careful to check for other factors that might be the actual cause of both the suspected cause and the suspected effect. If a person fails to check for the possibility of a common cause, then they will commit this fallacy. Thus, it is always a good idea to always ask "could there be a third factor that is actually causing both A and B?"

Click For Fallacy Description
10
Genetic Fallacy
Category: Fallacies of Relevance (Red Herrings) → Ad hominems (Genetic Fallacies)

A Genetic Fallacy is a line of "reasoning" in which a perceived defect in the origin of a claim or thing is taken to be evidence that discredits the claim or thing itself. It is also a line of reasoning in which the origin of a claim or thing is taken to be evidence for the claim or thing. This sort of "reasoning" has the following form:

  1. The origin of a claim or thing is presented.
  2. The claim is true(or false) or the thing is supported (or discredited).
It is clear that sort of "reasoning" is fallacious. For example: "Bill claims that 1+1=2. However, my parents brought me up to believe that 1+1=254, so Bill must be wrong."

It should be noted that there are some cases in which the origin of a claim is relevant to the truth or falsity of the claim. For example, a claim that comes from a reliable expert is likely to be true (provided it is in her area of expertise).

Click For Fallacy Description
4
Appeal to Spite
Category: Fallacies of Relevance (Red Herrings) → Distracting Appeals

The Appeal to Spite Fallacy is a fallacy in which spite is substituted for evidence when an "argument" is made against a claim. This line of "reasoning" has the following form:

  1. Claim X is presented with the intent of generating spite.
  2. Therefore claim C is false (or true)
This sort of "reasoning" is fallacious because a feeling of spite does not count as evidence for or against a claim. This is quite clear in the following case: "Bill claims that the earth revolves around the sun. But remember that dirty trick he pulled on you last week. Now, doesn't my claim that the sun revolves around the earth make sense to you?"

Of course, there are cases in which a claim that evokes a feeling of spite or malice can serve as legitimate evidence. However, it should be noted that the actual feelings of malice or spite are not evidence. The following is an example of such a situation:

Jill: "I think I'll vote for Jane to be treasurer of NOW."
Vicki: "Remember the time that your purse vanished at a meeting last year?"
Jill: "Yes."
Vicki: "Well, I just found out that she stole your purse and stole some other stuff from people."
Jill: "I'm not voting for her!"

In this case, Jill has a good reason not to vote for Jane. Since a treasurer should be honest, a known thief would be a bad choice. As long as Jill concludes that she should vote against Jane because she is a thief and not just out of spite, her reasoning would not be fallacious.

Click For Fallacy Description
9
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
12
Two Wrongs Make a Right

Two Wrongs Make a Right is a fallacy in which a person "justifies" an action against a person by asserting that the person would do the same thing to him/her, when the action is not necessary to prevent B from doing X to A. This fallacy has the following pattern of "reasoning":

  1. It is claimed that person B would do X to person A.
  2. It is acceptable for person A to do X to person B (when A's doing X to B is not necessary to prevent B from doing X to A).
This sort of "reasoning" is fallacious because an action that is wrong is wrong even if another person would also do it.

It should be noted that it can be the case that it is not wrong for A to do X to B if X is done to prevent B from doing X to A or if X is done in justified retribution. For example, if Sally is running in the park and Biff tries to attack her, Sally would be justified in attacking Biff to defend herself. As another example, if country A is planning to invade country B in order to enslave the people, then country B would be justified in launching a preemptive strike to prevent the invasion.

Click For Fallacy Description
1,081
↑ Answer Frequency
Example from LaBossiere's Forty Two Fallacies
Click These For Fallacy Descriptions ← Click these for fallacy descriptions.
Speed
Bonus
+
10
Pythagorean Triangle with Senses, Trivium, & Quadrivium
Winning in the Light Direction
Winning in the Light Direction
Winning in the Light Direction
Occupy Your Brain
Occupy Your Brain
Pop, Logic,
& Drop It

100 
Winning Flagpole Base
LOGIC
LOGIC
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Session Score
Your session will time out after about
24 minutes without playing.
Beginner
0
Medium
HIGH SCORES
JAO13,013
EAB12,147
WDB10,055
RCO9,051
DEB8,541









CONGRATS!
You've made it to the
top 100 high score list!

Don't stop now, but
your initials go here: