what is a pdf vs a cdf 
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 bottom lineprobability distribution functions pdfs describe the expected values of random variables drawn from a sample the shape of the pdf explains how likely it is that an observed value might occur stock prices and returns tend to follow a log normal distribution rather than a normal one indicating that downside losses are more frequent than very large gains relative to what the normal distribution would predict 