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Confusing Cause and Effect
AKA Questionable Cause, Reversing Causation

Category: Fallacies of Presumption → Casual Fallacies

Confusing Cause and Effect is a fallacy that has the following general form:

  1. A and B regularly occur together.
  2. Therefore A is the cause of B. This fallacy requires that there not be, in fact, a common cause that actually causes both A and B.
This fallacy is committed when a person assumes that one event must cause another just because the events occur together. More formally, this fallacy involves drawing the conclusion that A is the cause of B simply because A and B are in regular conjunction (and there is not a common cause that is actually the cause of A and B). The mistake being made is that the causal conclusion is being drawn without adequate justification.

In some cases it will be evident that the fallacy is being committed. For example, a person might claim that an illness was caused by a person getting a fever. In this case, it would be quite clear that the fever was caused by illness and not the other way around. In other cases, the fallacy is not always evident. One factor that makes causal reasoning quite difficult is that it is not always evident what is the cause and what is the effect. For example, a problem child might be the cause of the parents being short tempered or the short temper of the parents might be the cause of the child being problematic. The difficulty is increased by the fact that some situations might involve feedback. For example, the parents' temper might cause the child to become problematic and the child's behavior could worsen the parents' temper. In such cases it could be rather difficult to sort out what caused what in the first place.

In order to determine that the fallacy has been committed, it must be shown that the causal conclusion has not been adequately supported and that the person committing the fallacy has confused the actual cause with the effect. Showing that the fallacy has been committed will typically involve determining the actual cause and the actual effect. In some cases, as noted above, this can be quite easy. In other cases it will be difficult. In some cases, it might be almost impossible. Another thing that makes causal reasoning difficult is that people often have very different conceptions of cause and, in some cases, the issues are clouded by emotions and ideologies. For example, people often claim violence on TV and in movies must be censored because it causes people to like violence. Other people claim that there is violence on TV and in movies because people like violence. In this case, it is not obvious what the cause really is and the issue is clouded by the fact that emotions often run high on this issue.

While causal reasoning can be difficult, many errors can be avoided with due care and careful testing procedures. This is due to the fact that the fallacy arises because the conclusion is drawn without due care. One way to avoid the fallacy is to pay careful attention to the temporal sequence of events. Since (outside of Star Trek), effects do not generally precede their causes, if A occurs after B, then A cannot be the cause of B. However, these methods go beyond the scope of this program.

All causal fallacies involve an error in causal reasoning. However, this fallacy differs from the other causal fallacies in terms of the error in reasoning being made. In the case of a Post Hoc fallacy, the error is that a person is accepting that A is the cause of B simply because A occurs before B. In the case of the Fallacy of Ignoring a Common Cause A is taken to be the cause of B when there is, in fact, a third factor that is the cause of both A and B. For more information, see the relevant entries in this program.

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6
Post Hoc
Post Hoc Ergo Propter Hoc

AKA False Cause, Questionable Cause, Confusing Coincidental Relationships With Causes

Category: Fallacies of Presumption → Casual Fallacies

A Post Hoc is a fallacy with the following form:

  1. A occurs before B.
  2. Therefore A is the cause of B.
The Post Hoc fallacy derives its name from the Latin phrase "Post hoc, ergo propter hoc." This has been traditionally interpreted as "After this, therefore because of this." This fallacy is committed when it is concluded that one event causes another simply because the proposed cause occurred before the proposed effect. More formally, the fallacy involves concluding that A causes or caused B because A occurs before B and there is not sufficient evidence to actually warrant such a claim.

It is evident in many cases that the mere fact that A occurs before B in no way indicates a causal relationship. For example, suppose Jill, who is in London, sneezed at the exact same time an earthquake started in California. It would clearly be irrational to arrest Jill for starting a natural disaster, since there is no reason to suspect any causal connection between the two events. While such cases are quite obvious, the Post Hoc fallacy is fairly common because there are cases in which there might be some connection between the events. For example, a person who has her computer crash after she installs a new piece of software would probably suspect that the software was to blame. If she simply concluded that the software caused the crash because it was installed before the crash she would be committing the Post Hoc fallacy. In such cases the fallacy would be committed because the evidence provided fails to justify acceptance of the causal claim. It is even theoretically possible for the fallacy to be committed when A really does cause B, provided that the "evidence" given consists only of the claim that A occurred before B. The key to the Post Hoc fallacy is not that there is no causal connection between A and B. It is that adequate evidence has not been provided for a claim that A causes B. Thus, Post Hoc resembles a Hasty Generalization in that it involves making a leap to an unwarranted conclusion. In the case of the Post Hoc fallacy, that leap is to a causal claim instead of a general proposition.

Not surprisingly, many superstitions are probably based on Post Hoc reasoning. For example, suppose a person buys a good luck charm, does well on his exam, and then concludes that the good luck charm caused him to do well. This person would have fallen victim to the Post Hoc fallacy. This is not to say that all "superstitions" have no basis at all. For example, some "folk cures" have actually been found to work.

Post Hoc fallacies are typically committed because people are simply not careful enough when they reason. Leaping to a causal conclusion is always easier and faster than actually investigating the phenomenon. However, such leaps tend to land far from the truth of the matter. Because Post Hoc fallacies are committed by drawing an unjustified causal conclusion, the key to avoiding them is careful investigation. While it is true that causes precede effects (outside of Star Trek, anyway), it is not true that precedence makes something a cause of something else. Because of this, a causal investigation should begin with finding what occurs before the effect in question, but it should not end there.

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7
Misleading Vividness
Category: Fallacies of Relevance (Red Herrings) → Distracting Appeals

Misleading Vividness is a fallacy in which a very small number of particularly dramatic events are taken to outweigh a significant amount of statistical evidence. This sort of "reasoning" has the following form:

  1. Dramatic or vivid event X occurs (and is not in accord with the majority of the statistical evidence).
  2. Therefore events of type X are likely to occur.
This sort of "reasoning" is fallacious because the mere fact that an event is particularly vivid or dramatic does not make the event more likely to occur, especially in the face of significant statistical evidence.

People often accept this sort of "reasoning" because particularly vivid or dramatic cases tend to make a very strong impression on the human mind. For example, if a person survives a particularly awful plane crash, he might be inclined to believe that air travel is more dangerous than other forms of travel. After all, explosions and people dying around him will have a more significant impact on his mind than will the rather dull statistics that a person is more likely to be struck by lightning than killed in a plane crash.

It should be kept in mind that taking into account the possibility of something dramatic or vivid occurring is not always fallacious. For example, a person might decide to never go sky diving because the effects of an accident can be very, very dramatic. If he knows that, statistically, the chances of the accident are happening are very low but he considers even a small risk to be unacceptable, then he would not be making an error in reasoning.

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15
Appeal to Pity
Ad Misericordiam

Category: Fallacies of Relevance (Red Herrings) → Distracting Appeals

An Appeal to Pity is a fallacy in which a person substitutes a claim intended to create pity for evidence in an argument. The form of the "argument" is as follows:

  1. P is presented, with the intent to create pity.
  2. Therefore claim C is true.
This line of "reasoning" is fallacious because pity does not serve as evidence for a claim. This is extremely clear in the following case: "You must accept that 1+1=46, after all I'm dying..." While you may pity me because I am dying, it would hardly make my claim true.

This fallacy differs from the Appeal to the Consequences of a Belief (ACB). In the ACB fallacy, a person is using the effects of a belief as a substitute for evidence. In the Appeal to Pity, it is the feelings of pity or sympathy that are substituted for evidence.

It must be noted that there are cases in which claims that actually serve as evidence also evoke a feeling of pity. In such cases, the feeling of pity is still not evidence. The following is an example of a case in which a claim evokes pity and also serves as legitimate evidence:

Professor: "You missed the midterm, Bill."
Bill: "I know. I think you should let me take the makeup."
Professor: "Why?"
Bill: "I was hit by a truck on the way to the midterm. Since I had to go to the emergency room with a broken leg, I think I am entitled to a makeup."
Professor: "I'm sorry about the leg, Bill. Of course you can make it up."

The above example does not involve a fallacy. While the professor does feel sorry for Bill, she is justified in accepting Bill's claim that he deserves a makeup. After all getting run over by a truck would be a legitimate excuse for missing a test.

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18
Poisoning the Well
Category: Fallacies of Relevance (Red Herrings) → Ad hominems (Genetic Fallacies)

This sort of "reasoning" involves trying to discredit what a person might later claim by presenting unfavorable information (be it true or false) about the person. This "argument" has the following form:

  1. Unfavorable information (be it true or false) about person A is presented.
  2. Therefore any claims person A makes will be false.
This sort of "reasoning" is obviously fallacious. The person making such an attack is hoping that the unfavorable information will bias listeners against the person in question and hence that they will reject any claims he might make. However, merely presenting unfavorable information about a person (even if it is true) hardly counts as evidence against the claims he/she might make. This is especially clear when Poisoning the Well is looked at as a form of ad Hominem in which the attack is made prior to the person even making the claim or claims. The following example clearly shows that this sort of "reasoning" is quite poor.

Click For Fallacy Description
706
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.

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