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.

Peer Pressure

Peer Pressure is a fallacy in which a threat of rejection by one's peers (or peer pressure) is substituted for evidence in an "argument." This line of "reasoning" has the following form:

1. Person P is pressured by his/her peers or threatened with rejection.
2. Therefore person P's claim X is false.

This line of "reasoning" is fallacious because peer pressure and threat of rejection do not constitute evidence for rejecting a claim. This is especially clear in the following example:

Joe: "Bill, I know you think that 1+1=2. But we don’t accept that sort of thing in our group."
Bill: "I was just joking. Of course I don't believe that."

It is clear that the pressure from Bill's group has no bearing on the truth of the claim that 1+1=2.

It should be noted that loyalty to a group and the need to belong can give people very strong reasons to conform to the views and positions of those groups. Further, from a practical standpoint we must often compromise our beliefs in order to belong to groups. However, this feeling of loyalty or the need to belong simply do not constitute evidence for a claim.

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.

Appeal to Ridicule
AKA Appeal to Mockery, The Horse Laugh
Category: Fallacies of Relevance (Red Herrings) → Ad hominems (Genetic Fallacies)

The Appeal to Ridicule is a fallacy in which ridicule or mockery is substituted for evidence in an "argument." This line of "reasoning" has the following form:

1. X, which is some form of ridicule is presented (typically directed at the claim).
2. Therefore claim C is false.

This sort of "reasoning" is fallacious because mocking a claim does not show that it is false. This is especially clear in the following example: "1+1=2! That's the most ridiculous thing I have ever heard!"

It should be noted that showing that a claim is ridiculous through the use of legitimate methods (such as a non fallacious argument) can make it reasonable to reject the claim. One form of this line of reasoning is known as a "reductio ad absurdum" ("reducing to absurdity"). In this sort of argument, the idea is to show that a contradiction (a statement that must be false) or an absurd result follows from a claim. For example: "Bill claims that a member of a minority group cannot be a racist. However, this is absurd. Think about this: white males are a minority in the world. Given Bill's claim, it would follow that no white males could be racists. Hence, the Klan, Nazis, and white supremacists are not racist organizations."

Since the claim that the Klan, Nazis, and white supremacists are not racist organizations is clearly absurd, it can be concluded that the claim that a member of a minority cannot be a racist is false.

Fallacy of Composition
Category: Fallacies of Ambiguity

The fallacy of Composition is committed when a conclusion is drawn about a whole based on the features of its constituents when, in fact, no justification provided for the inference. There are actually two types of this fallacy, both of which are known by the same name (because of the high degree of similarity).

The first type of fallacy of Composition arises when a person reasons from the characteristics of individual members of a class or group to a conclusion regarding the characteristics of the entire class or group (taken as a whole). More formally, the "reasoning" would look something like this.

1. Individual F things have characteristics A, B, C, etc.
2. Therefore, the (whole) class of F things has characteristics A, B, C, etc.

This line of reasoning is fallacious because the mere fact that individuals have certain characteristics does not, in itself, guarantee that the class (taken as a whole) has those characteristics.

It is important to note that drawing an inference about the characteristics of a class based on the characteristics of its individual members is not always fallacious. In some cases, sufficient justification can be provided to warrant the conclusion. For example, it is true that an individual rich person has more wealth than an individual poor person. In some nations (such as the US) it is true that the class of wealthy people has more wealth as a whole than does the class of poor people. In this case, the evidence used would warrant the inference and the fallacy of Composition would not be committed.

The second type of fallacy of Composition is committed when it is concluded that what is true of the parts of a whole must be true of the whole without there being adequate justification for the claim. More formally, the line of "reasoning" would be as follows:

1. The parts of the whole X have characteristics A, B, C, etc.
2. Therefore the whole X must have characteristics A, B, C.

This sort of reasoning is fallacious because it cannot be inferred that simply because the parts of a complex whole have (or lack) certain properties that the whole that they are parts of has those properties. This is especially clear in math: The numbers 1 and 3 are both odd. 1 and 3 are parts of 4. Therefore, the number 4 is odd. It must be noted that reasoning from the properties of the parts to the properties of the whole is not always fallacious. If there is justification for the inference from parts to whole, then the reasoning is not fallacious. For example, if every part of the human body is made of matter, then it would not be an error in reasoning to conclude that the whole human body is made of matter. Similarly, if every part of a structure is made of brick, there is no fallacy committed when one concludes that the whole structure is made of brick.

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.