Refusal to Generalize

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Also Known As: Bad Apple Fallacy

Description:

This fallacy involves uncritically dismissing a significant number of examples or statistical evidence without adequately considering whether they would support a general claim. It has the following general form:

 

Premise 1: A significant number of examples or statistical evidence exist for generalization G.

Premise 2: The examples or statistical evidence is dismissed.

Conclusion: G is false.

 

This is fallacious because dismissing, without justification, a significant number of examples or statistical evidence does not prove that a claim is false. Refusing to consider such evidence for a general claim is as much a fallacy as leaping from inadequate evidence to accepting a general claim.

Since this fallacy has no logical force, its persuasive power must come from psychological sources. For example, someone who does not want to believe a general claim will be inclined to accept this fallacy. This fallacy can be used in conjunction with others. For example, someone might use an Ad Hominem attack to undermine the source of the examples, evidence or sample they are dismissing. As another example, someone might also fall for Wishful Thinking when rejecting a general claim they want to be false.

This fallacy is most used in bad faith; the person using it is intentionally refusing to generalize. It can also be used in good faith, in cases of ignorance or carelessness. For example, someone might note example after example of problems in their organization, yet not grasp the implications of having so many problems.

The fallacy also occurs when a person explicitly refuses to accept an adequate sample. An adequate sample is one that is large enough and representative enough to create a strong inductive generalization. This can be seen as the opposite of a Hasty Generalization (accepting a conclusion based on a sample that is too small). It has this form.

 

Premise 1: Sample S adequately supports generalization G.

Premise 2: S is ignored

Conclusion: G is not true.

 

This is poor reasoning because ignoring an adequate sample does not disprove a general claim. This is one case in which the error does indicate that the conclusion of a fallacy is probably false. If G is supported by a strong inductive generalization, then it is probably true. There is a version of this fallacy in which evidence is explicitly considered but is explained away; this is the Bad Apple Fallacy.

The Bad Apple Fallacy occurs when a significant number of examples or statistical evidence for a general claim is rejected by explaining away the examples or evidence as being rare cases, isolated incidents, or bad apples. It has the following two forms:

 

Premise 1: A significant number of examples or statistical evidence exist for generalization G.

Premise 2: The examples or statistical evidence is explained away as being rare cases, isolated incidents, or bad apples.

Conclusion: G is false.

 

Premise 1: Sample S adequately supports generalization G.

Premise 2: S is explained away as being made up of rare cases, isolated incidents, or bad apples.

Conclusion: G is not true.

 

The fallacy can occur when it is uncritically assumed that explaining the examples or evidence away disproves the general claim. In this case, the person committing the fallacy is not taking due care when rejecting the evidence. This can be done in good faith ignorance. As with any fallacy, the conclusion could turn out to be true; the problem is that it is not justified by the premises. If the examples or evidence is properly assessed and found to be inadequate, then this would not be fallacious reasoning.

The fallacy can also occur in bad faith when the person committing it is lying about the examples, statistical evidence or sample being made up of rare cases, isolated incidents, or bad apples. In this case, the error of reasoning is joined by the act of deceit. This tactic can be very effective when the target audience is ignorant of the evidence or wants to believe the conclusion.

For example, someone might not want to believe that sexual assault is a problem in United States military and hence be inclined to reject examples and data as isolated incidents or a few bad apples.

The use of the phrase “a few bad apples” is popular when someone commits this fallacy while attempting to explain away or dismiss evidence or examples of bad behavior. This can be an effective rhetorical strategy. When the person admits that there have been problems, they can seem reasonable and create a more defensible position: they are not claiming that there are no problems. Explaining away or dismissing the problems as bad apples can be appealing, especially when the target audience is ignorant of the facts or already inclined to want to reject the general claim, perhaps because of a favorable or unfavorable view of the subject of the generalization.  Ironically, while the bad apple phrase is used to claim that there is not a general problem, the original phrase is “one bad apple spoils the whole barrel.”

 

 

Defense: The main defense against inflicting this fallacy on yourself is to be careful about rejecting examples or statistical evidence too quickly. While it is an error to rush to a Hasty Generalization or accept Anecdotal Evidence, being excessively cautious about generalizing can lead to committing this fallacy.

To avoid falling for this fallacy when used by others, the defense is to consider whether they are dismissing examples, statistical evidence, or a seemingly adequate sample without due care. While terms such as “isolated incidents” and “bad apples” can be used in good faith, these terms can be red flags indicating that the fallacy is being employed. If there are repeated “isolated incidents” and a barrel of “bad apples” being dismissed, then this suggests that the fallacy is being committed intentionally.

 

Example #1

Reporter: “Your opponent says they support police reform because they are concerned with the number of cases involving excessive use of force, including lethal force. What is your reply?”

Senator Wiggum: “While there have been regrettable incidents, these are very rare and no reason to be worried about policing in general. That is why I support re-funding the police.”

Reporter: “What about all the incidents that have been reported and documented?”

Senator Wiggum: “Those are just bad apples.”

Reporter: “That seems more like a spoiled barrel.”

Senator Wiggum: “Hah. Fake news.”

 

Example #2

Reporter: “Your opponent says they support legislation that will forbid insider trading by members of congress. What do you think of that?”

Speaker Nancy: “While there have been some unfortunate incidents, these are very rare and no reason to be worried. The existing laws are working.”

Reporter: “What about all the incidents that have been reported and documented?

Speaker Nancy: “Those are just a few bad apples.”

Reporter: “That seems more like a spoiled barrel.”

Speaker Nancy: “Hah.”

 

Example #3

Malcolm: “Racism is still a serious problem in America.”

Jefferson: “I agree it was a problem in the 1960s, but there is not much racism today.”

Malcolm: “I’ve complied a database of evidence, complete with documentation and cited sources. If you have a few hours, you can skim through it.”

Jefferson: “Well, in a big country there will be some racists. But racism is not a big problem today.”

 

Example #4

Malcolm: “Men face some serious problems today.”

Lacy: “Oh God, are you going to go into some rant about how men are the real victims?”

Malcolm: “No. My point is that men face some serious issues because they are men. I am not downplaying the problems women face. But I think that issues involving men such as violence, wages, education, and parental rights are often ignored.  I’ve complied a database of evidence, complete with documentation and cited sources. If you have a few hours, you can skim through it.”

Lacy: “Well, in a big country some men will face real problems. But it is absurd to think that men, in general, face such problems. I mean, this is a patriarchy. Men have it easy.”

 

Example #5

Harvey: “I’m concerned about the number of birds being killed by wind turbines.”

Celina: “Oh, a few birds do get killed now and then. That is sad, but hardly a massacre.”

Harvey: “I’ve seen some credible estimates that place it over 200,000 per year. And that might just be a sample. There could be even more.”

Celina: “That sounds way too high. I am sure that it just speculation by people who hate renewable energy or are being paid by the fossil fuel industry.”

Harvey: “At least look at the data.”

Celina: “Nah, I am sure it is biased.”

Originally appeared on A Philosopher’s Blog Read More

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