A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

For a discrete distribution, probabilities can be assigned to the values in the distribution - for example, "the probability that the web page will have 12 clicks in an hour is 0.15." In contrast, a continuous distribution has an infinite number of possible values, and the probability associated with any particular value of a continuous distribution is null. Therefore, continuous distributions are normally described in terms of probability density, which can be converted into the probability that a value will fall within a certain range.

For a discrete distribution, probabilities can be assigned to the values in the distribution - for example, "the probability that the web page will have 12 clicks in an hour is 0.15." In contrast, a continuous distribution has an infinite number of possible values, and the probability associated with any particular value of a continuous distribution is null. Therefore, continuous distributions are normally described in terms of probability density, which can be converted into the probability that a value will fall within a certain range.