A statistical concept in which the likelihood of an event is predicated on a set of conditions. Conditional probabilities are expressed as p(A½B), which may be read as: what is the probability that event A will occur given event B has occurred? More specifically, a false negative polygraph result can be represented as p(no evidence of DI responses½the person is lying). Conditional probabilities are important when characterizing the accuracy of PDD. The following illustration is one offered by critics of the use of PDD in screening. Suppose that PDD is 90% accurate in detecting both deception and truthfulness. Also, assume that it is used to test 1,000 government employees, only one of whom is involved in the activity of interest, say, treason. There is a 90% chance that the one guilty person will be caught. Of the 999 innocent employees, 899 (90%) will pass the examination, and the remaining 100 will be false positives. The ratio of true positives (1 guilty) versus false positives (100 innocent) is a low payoff if the consequences are employment termination or criminal prosecution. Not used in this example is the influence of repeated testing and other methods that can reduce false positives, but it is clear from this example that PDD validity estimates are not well represented by a single percentage. See base rates.