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The PDF and CDF are interrelated concepts in probability theory. The PDF gives the probability of a continuous random variable taking on a specific value. At the same time, the CDF provides the cumulative probability of the random variable being less than or equal to a given value.

PDFs are usually plotted on a graph that typically resembles a bell curve, with the data lying below the curve. A skewed curve at either end indicates greater or lesser risk/reward.

PDF and CDF of The Normal Distribution The CDF is the integration, from left to right, of the PDF. Using these two normal distribution functions, we can calculate different types of probability estimates from our normally distributed data.

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 will be less than or equal to a certain value.

4.1: Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) for Continuous Random Variables.

Probability mass functions (pmf) are used to describe discrete probability distributions. While probability density functions (pdf) are used to describe continuous probability distributions.

The probability density function (PDF) is a statistical expression that defines the probability that some outcome will occur. In this function, the probability is the percentage of a dataset's distribution that falls between two criteria.