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What is the difference between CDF and PDF?

Are the cumulative distribution functions and probability density functions different in the sense that they represent the same thing? What are the advantages and disadvantages of each one?

A:

The PDF is the derivative of the CDF. The CDF is the cumulative distribution function of a continuous probability distribution. It may also refer to the entire family of probability distributions on the real line, which has probability density functions for each value.
The PDF may refer to the entire family of continuous probability distributions, which may or may not have a CDF. The PDF is the probability density function at every value, and the whole family of probability distributions.
In case you’re wondering about discrete probability distributions, the CDF is called the cumulative distribution function or probability function, where the $p$ is the probability of the event.

A:

Cumulative distribution functions and probability density functions are not exactly the same thing.
One difference is that the CDF tells you whether or not a random variable (a specific variable that has a specific value each time a random experiment is conducted) is greater than a specific value, and the PDF tells you what that value is (how many times greater).
For instance, lets say we throw a dart at a dartboard. What is the probability that it falls in the top-half
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