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Probability Mass Function (PMF): Easy Definition, Examples

Probability > Probability Mass Function

You may find it helpful to read this other article first: Discrete vs. Continuous variables.

What is a Probability Mass Function?

A probability mass function (PMF) gives you probabilities for discrete random variables. “Random variables” are variables from experiments like dice rolls, choosing a number out of a hat, or getting a high score on a test. The “discrete” part means that there’s a set number of outcomes. For example, you can only roll a 1,2,3,4,5, or 6 on a die.

Its counterpart is the probability density function, which gives probabilities for continuous random variables.

A PMF can be an equation, a table, or a graph.


A PMF equation looks like this:
P(X = x).
That just means “the probability that X takes on some value x”.
It’s not a very useful equation on its own; What’s more useful is an equation that tells you the probability of some individual event happening. For example:
P(X=1) = 0.2 * 0.2.

How you come up with these equations depends mostly on what type of event you have. For example, the binomial distribution PMF is:
binomial pmf

And the Poisson distribution PMF is:
poisson pmf

Tables and Graphs

The histogram is a graph of a PMF.
make a histogram

On the x-axis are the discrete random variables; On the y-axis are the probabilities for each discrete variable. The area under a graph of a probability mass function is 100% (i.e. the probability of all events, when added together, is 100%). The above histogram shows:

  • 10% of people scored between 20 and 29,
  • 20% of people scored between 70 and 80,
  • 40% of people scored between 80 and 90, and
  • 30% of people scored between 90 and 100.

That gives a total of 10% + 20% + 40% + 30% = 100%.

Another example:
The following table and graph show the PMF for an experiment. Note how all of the probabilities add up to 1:
probability mass function PMF

Formal Definition

Like most statistical terms, there’s the informal definition, and then there’s the formal one:

The probability mass function, f(x) = P(X = x), of a discrete random variable X has the following properties:

  1. All probabilities are positive: fx(x) ≥ 0.
  2. Any event in the distribution (e.g. “scoring between 20 and 30”) has a probability of happening of between 0 and 1 (e.g. 0% and 100%).
  3. The sum of all probabilities is 100% (i.e. 1 as a decimal): Σfx(x) = 1.
  4. An individual probability is found by adding up the x-values in event A. P(X Ε A) =


Terminology note

The general use of the term PMF means a probability distribution for a discrete random variable. However, some authors (not many) use the term “probability mass function” to mean either a discrete or continuous probability distribution. To add to the confusion, other authors might call a PMF a probability function or frequency function.


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Probability Mass Function (PMF): Easy Definition, Examples was last modified: October 15th, 2017 by Stephanie Glen