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Jumping to conclusions

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Jumping to conclusions is a psychological term that refers to delusional reasoning.[1] It is also a philosophical term that refers to... delusional reasoning.[2][3] In psychology, these delusions are seen as indicators of psychosis. In philosophy, these delusions are seen as indicators of human stupidity.

In psychology[edit]

Psychological researchers have shown that the jumping to conclusions delusion is associated with reduced data gathering.[1] Considering that one of the cognitive models for rationality is that our brains use Bayesian reasoning, a reduction in data gathering would result in rational errors as this statistical methodology becomes more accurate as more data becomes available. Some examples of jumping to conclusionsWikipedia delusions are mind reading and fortune telling. One suggested treatment for these delusions is reasoning training.[1] Whether people like Donald Trump are suffering from psychosis and are therefore in need of reasoning training remains a controversial subject.

In philosophy[edit]

In philosophy, there are several fallacies that can be placed under the category of jumping to conclusions. The two main ones are hasty generalization and the accident fallacy.

Hasty generalization[edit]

See the main article on this topic: Overgeneralization

Hasty generalization is essentially a description of what people do when they are suffering from the jumping to conclusions delusion, as this is the fallacy that occurs when one makes generalizations with too little data. If one makes a conclusion based off of anecdotal evidence, a small sample size, or simply using an unrepresentative sample due to poor data collection techniques, one is committing this fallacy. Given that probability relies on good data and is often Bayesian, these types of fallacies are closely associated with probabilistic fallacies. Basing probability off of poor data will result in poor statistical conclusions. A more esoteric fallacy of this type is the ludic fallacy which occurs when one makes a hasty generalization about the statistical model one is using as opposed to the data itself. The classic example of this is basing one's probability on the assumption that a coin is fair when it is not an evenly weighted coin.

Accident fallacy[edit]

See the main article on this topic: Fallacy of accident

The accident fallacy is essentially the inverse of the hasty generalization fallacy. This is the fallacy that occurs when one takes a generalization (a.k.a. a rule of thumb) and assumes that the generalization applies to all situations. The two main subcategories of this fallacy are the ecological fallacy and stereotyping. Because people are stupid, many people use these terms inappropriately. Thus the following real world example will be used to explain what these things are.[note 1]

There was a truck driver who only spoke Spanish and person X had to tell him where to go and what to do, but person X didn't speak Spanish. Now he was chummy with Hispanic person Y, and while he didn't know if person Y spoke Spanish or not, he needed a translator and none of the other people around him were Hispanic. He then said something to person Y like "You wouldn't happen to speak Spanish, would you?" And, lo and behold, he did! And person Y told the driver where to go and what to do. Problem solved. Now the more liberal types may argue, "How dare person X use stereotypes with person Y!" but they would be being fallacious because person X didn't use stereotypes. Stereotype fallacies only occur when someone believes something like "All Hispanic people speak Spanish." which was not a view held by person X, as he only believed that there was a good chance that person Y spoke Spanish. An argument could be made that person X made an ecological fallacy as he applied group data about group "Hispanic" to conclude that person Y might speak Spanish and that group data may have not been true for person Y. Then again, that conclusion is not really accurate either because person X was right about person Y speaking Spanish, and thus it could be argued that no fallacy was committed. So what occurred? Well, what occurred was person X was being rational. He knew he needed a Spanish translator, and because person Y was Hispanic, he knew his best option would be to ask person Y if he spoke Spanish. If the answer was no instead of a yes, then person X would have had to turn to his next best option, which may have been hand signals or possibly Google translate. That is how rational minds work. Irrational minds, on the other hand, will commit the stereotype fallacy. For example, the aforementioned liberal may argue "All people who believe Hispanics speak Spanish are guilty of using stereotypes." That is not true as person X proves. The liberal who made this argument, on the other hand, would be guilty of using stereotypes (and also a double standard fallacy).

See also[edit]

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  1. This actually occurred with AndyChrist (talk) 19:56, 26 May 2018 (UTC).