# Continuous Probability Distribution versus Discrete Probability Distribution

Discrete probability distributions comes into play when there are a finite number of discrete possible events. You can assign a probability to the discrete events and the sum of the probability of all the events must be 1. A standard example is the roll of a dice. The variable (dice roll) takes on certain values. There are six possible events/outcomes, each of the numbers 1 through 6. Assuming a fair dice, each of the numbers are equally likely. Rolling the number 2 takes a probability of $\frac{1}{6}$.