# Overview for Probability Distributions

Use Probability Distributions to calculate the values of a probability density function (PDF), cumulative distribution function (CDF), or inverse cumulative distribution function (ICDF) for many different data distributions.
Probability density function (PDF)

The probability density function (PDF) is an equation that represents the probability distribution of a continuous random variable. The PDF curve indicates regions of higher and lower probabilities for values of the random variable. For example, for a normal distribution, the highest PDF value is at the mean, and lower PDF values are in the tails of the distribution.

For a discrete distribution, such as a binomial distribution, you can use the PDF to determine the probability of exact data values (also called the Probability Mass Function or PMF).

Cumulative distribution function (CDF)

The cumulative distribution function (CDF) calculates the cumulative probability for a given x-value. Use the CDF to determine the probability that a random observation that is taken from the population is less than or equal to a certain value. You can also use this information to determine the probability that an observation is greater than a certain value, or between two values. For example, a cumulative distribution function can show the proportion of trees in a forest that have diameter measurements of 10 inches or less.