If you want to take your understanding of probabilities to the next level, it’s crucial to be familiar with the concept of a **probability distribution**.

In short, a probability distribution is an __assignment of probabilities or probability densities__ to __all possible outcomes__ of a __random variable__.

For example, take the random process of flipping a regular coin. The outcome of each flip is a random variable with a probability distribution:

- P(“Heads”) = 0.5
- P(“Tails”) = 0.5

Depending on the type of random variable you’re working with, there are two general types of probability distributions: **discrete** and **continuous**. In this post, I’m going to give an overview of both kinds. And in follow-up posts I’m going to individually introduce specific frequently used probability distributions from each kind.