A probability density function (PDF) explains which values are likely to appear in a data-generating process at any given time or for any given draw. A cumulative distribution function (CDF) instead depicts how these marginal probabilities add up, ultimately reaching 100% (or 1.0) of possible outcomes. Using a CDF we can see how likely it is that a variable's outcome will be less than or equal to some predicted value. The figure below, for example, shows the CDF for a normal distribution.