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.

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.

Guilt by Association
AKA Bad Company Fallacy, Company that You Keep Fallacy
Category: Fallacies of Relevance (Red Herrings) → Ad hominems (Genetic Fallacies)

Guilt by Association is a fallacy in which a person rejects a claim simply because it is pointed out that people she dislikes accept the claim. This sort of "reasoning" has the following form:

1. It is pointed out that person A accepts claim P.
2. Therefore P is false

It is clear that sort of "reasoning" is fallacious. For example the following is obviously a case of poor "reasoning": "You think that 1+1=2. But, Adolf Hitler, Charles Manson, Joseph Stalin, and Ted Bundy all believed that 1+1=2. So, you shouldn't believe it."

The fallacy draws its power from the fact that people do not like to be associated with people they dislike. Hence, if it is shown that a person shares a belief with people he dislikes he might be influenced into rejecting that belief. In such cases the person will be rejecting the claim based on how he thinks or feels about the people who hold it and because he does not want to be associated with such people.

Of course, the fact that someone does not want to be associated with people she dislikes does not justify the rejection of any claim. For example, most wicked and terrible people accept that the earth revolves around the sun and that lead is heavier than helium. No sane person would reject these claims simply because this would put them in the company of people they dislike (or even hate).

False Dilemma
AKA Black & White Thinking
Category: Fallacies of Presumption

A False Dilemma is a fallacy in which a person uses the following pattern of "reasoning":

1. Either claim X is true or claim Y is true (when X and Y could both be false).
2. Claim Y is false.
3. Therefore claim X is true.

This line of "reasoning" is fallacious because if both claims could be false, then it cannot be inferred that one is true because the other is false. That this is the case is made clear by the following example:

1. Either 1+1 =4 or 1+1=12.
2. It is not the case that 1+1 = 4.
3. Therefore 1+1 =12.

In cases in which the two options are, in fact, the only two options, this line of reasoning is not fallacious. For example:

1. Bill is dead or he is alive.
2. Bill is not dead.
3. Therefore Bill is alive.

Appeal to Belief
Category: Fallacies of Relevance (Red Herrings)

Appeal to Belief is a fallacy that has this general pattern:

1. Most people believe that a claim, X, is true.
2. Therefore X is true.

This line of "reasoning" is fallacious because the fact that many people believe a claim does not, in general, serve as evidence that the claim is true.

There are, however, some cases when the fact that many people accept a claim as true is an indication that it is true. For example, while you are visiting Maine, you are told by several people that they believe that people older than 16 need to buy a fishing license in order to fish. Barring reasons to doubt these people, their statements give you reason to believe that anyone over 16 will need to buy a fishing license.

There are also cases in which what people believe actually determines the truth of a claim. For example, the truth of claims about manners and proper behavior might simply depend on what people believe to be good manners and proper behavior. Another example is the case of community standards, which are often taken to be the standards that most people accept. In some cases, what violates certain community standards is taken to be obscene. In such cases, for the claim "x is obscene" to be true is for most people in that community to believe that x is obscene. In such cases it is still prudent to question the justification of the individual beliefs.

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.