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The base rate fallacy is committed when a person focuses on specific information and ignores generic information relating to the overall likelihood of a given event. A simple example of this would involve the diagnosis of a condition in a patient. The generic information would relate to the prevalence of the condition in the population as a whole, and the specific information would be that garnered from tests and examinations of one specific patient. The base rate fallacy is committed if the doctor focuses on the result of the test and ignores the overall likelihood of the event.
Fallacies are identified logic-traps, which lead the thinker or listener into coming to erroneous conclusions. One example of a fallacy is the motive fallacy, which is often used in political arguments to discredit a particular line of reasoning. For example, one politician may argue that nuclear weapons are expensive, dangerous, and should be scrapped. An opposing politician could respond by saying that the only reason he is arguing that is because he is trying to lobby favor from extreme liberals. The first politician’s motive is irrelevant to the accuracy of his statement: nuclear weapons are still expensive and dangerous, and therefore the original point still stands.
An example of the base rate fallacy can be constructed using a fictional fatal disease. Imagine that this disease affects one in 10,000 people, and has no cure. A test is developed to determine who has the condition, and it is correct 99 percent of the time. John takes the test, and his doctor solemnly informs him that the results came up positive; however, John is not concerned. Understanding why is vital to understanding the base rate fallacy.
If the test is only 99 percent accurate, one in 100 people who take the test will receive an incorrect result, and 99 will receive the correct result. It is important to remember that only one in 10,000 people have the condition. If one million people take the test, only around 100 people will have the condition, and 999,900 people will not have it. One percent of the people who do not have the condition, 9,999 people, will be told that they do have it as a result of the accuracy of the test. It is 100 times more likely that John will be one of the 9,999 people incorrectly identified as having the condition rather than the 99 people correctly identified as having it.
The specific information, John’s test, is therefore shown to be probably incorrect as a result of the base rate. The base rate fallacy can be avoided if all of the available information is correctly studied before a decision is reached. The information about the overall likelihood of a given event should be taken alongside specific information to reach the logical conclusion.
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