A discrete probability distribution useful for characterizing the time between Bernoulli trials For example, suppose machine parts are characterized as defective or non-defective, and let the probability of a defective part equal p If you begin testing a sample of parts to find a defective, then the number of parts which must be tested before the first defective is found follows a geometric distribution Parameters: event probability 0<=p<=1 Domain: X=0,1,2, Mean: (1-p)/p Variance: (1-p)/(p^2)
The geometric distribution describes the number of trials up to and including the first success, in independent trials with the same probability of success The geometric distribution depends only on the single parameter p, the probability of success in each trial For example, the number of times one must toss a fair coin until the first time the coin lands heads has a geometric distribution with parameter p = 50% The geometric distribution assigns probability p×(1 - p)k-1to the event that it takes k trials to the first success The expected value of the geometric distribution is 1/p, and its SE is (1-p)½/p