What is a CDF?

CDF stands for cumulative distribution function. The CDF complements the Probability Density Function and provides a cumulative view of the probabilities linked to a random variable. Unlike the smooth curve of the PDF, the CDF appears as a step function, jumping at specific values. It shows the probability that a random variable will be less than or equal to a given value.

Starting from 0 for negative values, the CDF gradually increases to 1 as the value of the random variable increases. For discrete random variables, the CDF rises in steps, corresponding to the probabilities of each possible outcome. With continuous random variables, it increases smoothly and reflects the combined probabilities across different intervals.

Cumulative Distribution Function or CDF and the Probability Density Function or PDF are important in statistics when dealing with continuous random variables. While both functions provide insights into probabilities, they have different purposes and give different perspectives on the distribution of data.

In this article we will discuss about the difference between Cumulative Distribution Function and the Probability Density Function in detail.