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A probability mass function (PMF) gives the probability of each possible value of a discrete random variable. A probability density function (PDF) describes a continuous random variable; to find a probability from it, integrate the density over a range. A density value at one point is not the probability of that point. The cumulative distribution function (CDF) provides a common way to express probabilities for either kind of variable.

PMF vs. PDF: the key difference

Question Probability mass function (PMF) Probability density function (PDF)
Used for A discrete random variable with finite or countable possible values A continuous random variable represented by a density
What the function gives For a supported value x, p(x) = P(X = x) The density at x, not P(X = x)
How to calculate an event probability Add the masses for the values in the event Integrate the density over the event’s interval or region
How it is normalized The masses sum to 1 The density integrates to 1 over its domain
Probability at one exact value Can be positive for a supported value Is zero for an individual point when the variable has a continuous density
Example A die roll A measured lifetime, distance, or weight

These definitions follow Boston University’s probability and statistics refresher, Penn State STAT 500, and Penn State STAT 414.

What a PMF tells you

For a discrete random variable X, its probability mass function is defined by p(x) = P(X = x). It assigns a probability to each possible value in the variable’s support. A valid PMF is nonnegative, assigns zero probability outside the support, and has masses that sum to 1.

Example: one fair die roll

Let X be the number of spots on one roll of a fair six-sided die. The possible values are 1, 2, 3, 4, 5, and 6, each with probability 1/6. To find P(X ≤ 2), add the masses for 1 and 2: 1/6 + 1/6 = 1/3.

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More generally, for a set of values A, calculate P(X ∈ A) by summing p(x) across the values x in A. The notation for a PMF can vary: some sources use p(x), while others use f(x). What matters is that, in the discrete case, the function’s value is the probability of the corresponding outcome.

What a PDF tells you—and why its height is not a probability

A probability density function describes a continuous random variable. A valid density is nonnegative and integrates to 1 over its domain. Its value f(x) is a density, not the probability that X equals x. For a variable with a continuous density, P(X = x) = 0 for any single value x.

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To find the probability that X falls between a and b, integrate the density across that interval: P(a ≤ X ≤ b) = ∫ab f(x) dx. Geometrically, this integral is the area under the density curve over the interval. A density height can even exceed 1; that does not violate the probability rules, because probability is determined by area, not height alone.

Example: a measurement

Suppose X represents a randomly selected hamburger’s weight. A question such as “What is the chance that X is between 0.20 and 0.30 pounds?” asks for the probability across an interval, calculated by integrating the density from 0.20 to 0.30. The exact weight of 0.25 pounds is a single point, so it has probability zero under a continuous-density model.

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The same reasoning applies to continuous models of quantities such as lifetime or distance. Measurement values may be recorded to a particular precision, but that does not make the underlying continuous model a PMF over every recorded decimal.

How the CDF connects both cases

The cumulative distribution function (CDF) is F(x) = P(X ≤ x). It gives the probability that the variable is at or below a threshold.

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  • For a discrete variable, the CDF adds the PMF values up through x.
  • For a continuous variable with a PDF, the CDF integrates the density up through x.

Where the CDF is differentiable, its derivative is the PDF. The CDF therefore offers a shared cumulative view, although the PMF and PDF themselves answer different kinds of probability questions.

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How to choose the right function

  1. Identify what X represents. If its possible values are countable outcomes, such as the spots on a die, use the discrete framework. If it measures a quantity such as weight or distance, a continuous framework may be appropriate. OpenStax explains this distinction through counting versus measuring, and Penn State STAT 414 discusses countable and uncountable supports.
  2. Ask whether the question is about an exact value or a range. For a discrete variable, a PMF can give the probability of an exact supported value. For a continuous variable with a density, find the probability over an interval by integration.
  3. Check the normalization. Confirm that the PMF’s probabilities sum to 1 or that the PDF integrates to 1 over the full domain.
  4. Use the CDF for cumulative questions. For questions phrased as “at most x,” calculate or read F(x) = P(X ≤ x), using a cumulative sum for a PMF or a cumulative integral for a PDF.

Clarifying the term “probability distribution function”

“Probability distribution function” is not precise enough by itself to identify which function is meant. Specify whether you mean a probability mass function (PMF), a probability density function (PDF), or a cumulative distribution function (CDF). In this article, PDF means probability density function, not the document-file format.

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The PMF-versus-PDF comparison describes the standard discrete and continuous cases. It is a useful first distinction, not a claim that every possible probability distribution must fit one of those two elementary forms.

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