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The base rate fallacy occurs when specific information is focused on and general information is ignored, leading to incorrect conclusions. An example is diagnosing a patient. The fallacy can be avoided by considering all available information. Another example is the motive fallacy, used to discredit reasoning.
The base rate fallacy is made when a person focuses on specific information and ignores general information related to the overall probability of a certain event. A simple example of this would involve diagnosing a condition in a patient. Generic information would concern the prevalence of the condition in the population as a whole, and specific information would be that obtained from tests and examinations of a specific patient. The baseline rate error is made if the doctor focuses on the test result and ignores the overall probability of the event.
Fallacies are identified logical pitfalls, which lead the thinker or listener to come to incorrect conclusions. An example of a fallacy is the motive fallacy, which is often used in political discussions to discredit a particular line of reasoning. For example, a politician might argue that nuclear weapons are expensive, dangerous, and should be eliminated. An opposing politician might respond by saying that the only reason he’s debating is because he’s trying to lobby for favors from extreme liberals. The first politician’s plea is irrelevant to the accuracy of his statement: Nuclear weapons are still expensive and dangerous, and therefore the original point still holds.
An example of the base rate error can be constructed using an imaginary fatal disease. Imagine this disease affects one in 10,000 people and has no cure. A test is developed to determine who has the condition and is correct 99% of the time. John takes the test and his doctor solemnly informs him that the results are positive; however, John isn’t worried. Understanding why is critical to understanding the base rate fallacy.
If the test is only 99% accurate, one in 100 people who take it will get an incorrect result, and 99 will get the correct result. It’s important to remember that only one in 10,000 people have the condition. If one million people get tested, only about 100 people will have the condition and 999,900 people won’t have it. The one percent of people who don’t have the condition, 9,999 people, will be told they have it because of the accuracy of the test. John is 100 times more likely to be one of the 9,999 people incorrectly identified as having the condition than the 99 people correctly identified as having the condition.
The specific information, John’s test, then turns out to be probably wrong because of the base rate. The base rate error can be avoided if all available information is studied properly before making a decision. Information about the overall probability of a given event should be taken along with specific information to reach the logical conclusion.
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