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To see this, let's walk through an example. Assume a simple database -- name and a single code indicating "innocent" or "guilty." When a policeman encounters someone, he looks that person up in the database, and then arrests him if the database says "guilty."
Example 1: Assume the database is 100% accurate. If that is the case, there won't be any false arrests because of bad data. It works perfectly.
Example 2: Assume a 0.0001% error rate: one error in a million. (An error is defined as a person having an "innocent" code when he is guilty, or a "guilty" code when he is innocent.) Furthermore, assume that one in 10,000 people are guilty. In this case, for every 100 guilty people the database correctly identifies it will mistakenly identify one innocent person as guilty (because of an error). And the number of guilty people erroneously listed as innocent is tiny: one in a million.
Example 3: Assume a 1% error rate -- one in a hundred -- and the same one in 10,000 ratio of guilty people. The results are very different. For every 100 guilty people the database correctly identifies, it will mistakenly identify 10,000 innocent people as guilty. The number of guilty people erroneously listed as innocent is larger, but still very small: one in 100. Link [Boing Boing Blog]
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Copyright 2003 Karlin Lillington
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