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Select the one clearest logical fallacy in the example,
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John: "Sally was saying that people shouldn't hunt animals or kill them for food or clothing. She also..."
Wanda: "Well, Sally is a sissy crybaby who loves animals way too much."
John: "So?"
Wanda: "That means she is wrong about that animal stuff. Also, if we weren't supposed to eat 'em, they wouldn't be made of meat."
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Appeal to Novelty
AKA Appeal to the New, Newer is Better, Novelty

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

Appeal to Novelty is a fallacy that occurs when it is assumed that something is better or correct simply because it is new. This sort of "reasoning" has the following form:

  1. X is new.
  2. Therefore X is correct or better.
This sort of "reasoning" is fallacious because the novelty or newness of something does not automatically make it correct or better than something older. This is made quite obvious by the following example: Joe has proposed that 1+1 should now be equal to 3. When asked why people should accept this, he says that he just came up with the idea. Since it is newer than the idea that 1+1=2, it must be better.

This sort of "reasoning" is appealing for many reasons. First, "western culture" includes a very powerful commitment to the notion that new things must be better than old things. Second, the notion of progress (which seems to have come, in part, from the notion of evolution) implies that newer things will be superior to older things. Third, media advertising often sends the message that newer must be better. Because of these three factors (and others) people often accept that a new thing (idea, product, concept, etc.) must be better because it is new. Hence, Novelty is a somewhat common fallacy, especially in advertising.

It should not be assumed that old things must be better than new things (see the fallacy Appeal to Tradition) any more than it should be assumed that new things are better than old things. The age of a thing does not, in general, have any bearing on its quality or correctness (in this context).

Obviously, age does have a bearing in some contexts. For example, if a person concluded that his day old milk was better than his two‐month old milk, he would not be committing an Appeal to Novelty. This is because in such cases the newness of the thing is relevant to its quality. Thus, the fallacy is committed only when the newness is not, in and of itself, relevant to the claim.

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2
Ad Hominem
AKA Ad Hominem Abusive, Personal Attack

Category: Fallacies of Relevance (Red Herrings) → Ad hominems (Genetic Fallacies)

Translated from Latin to English, "ad Hominem" means "against the man" or "against the person."

An ad Hominem is a general category of fallacies in which a claim or argument is rejected on the basis of some irrelevant fact about the author of or the person presenting the claim or argument. Typically, this fallacy involves two steps. First, an attack against the character of person making the claim, her circumstances, or her actions is made (or the character, circumstances, or actions of the person reporting the claim). Second, this attack is taken to be evidence against the claim or argument the person in question is making (or presenting). This type of "argument" has the following form:

  1. Person A makes claim X.
  2. Person B makes an attack on person A.
  3. Therefore A's claim is false.
The reason why an ad Hominem (of any kind) is a fallacy is that the character, circumstances, or actions of a person do not (in most cases) have a bearing on the truth or falsity of the claim being made (or the quality of the argument being made).

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443
Burden of Proof
Ad Ignorantiam

AKA Appeal to Ignorance

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

Burden of Proof is a fallacy in which the burden of proof is placed on the wrong side. Another version occurs when a lack of evidence for side A is taken to be evidence for side B in cases in which the burden of proof actually rests on side B. A common name for this is an Appeal to Ignorance. This sort of reasoning typically has the following form:

  1. Claim X is presented by side A and the burden of proof actually rests on side B.
  2. Side B claims that X is false because there is no proof for X.
In many situations, one side has the burden of proof resting on it. This side is obligated to provide evidence for its position. The claim of the other side, the one that does not bear the burden of proof, is assumed to be true unless proven otherwise. The difficulty in such cases is determining which side, if any, the burden of proof rests on. In many cases, settling this issue can be a matter of significant debate. In some cases the burden of proof is set by the situation. For example, in American law a person is assumed to be innocent until proven guilty (hence the burden of proof is on the prosecution). As another example, in debate the burden of proof is placed on the affirmative team. As a final example, in most cases the burden of proof rests on those who claim something exists (such as Bigfoot, psychic powers, universals, and sense data).

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6
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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6
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.

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19
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.

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