Difference Between CDF and PDF
The difference between CDF and PDF can be understood from the table given below.
Cumulative Distribution Function | Probability Density Function |
|---|---|
It gives the probability that a random variable is less than or equal to a specific value. | It describes the likelihood of a random variable falling within a small interval around a particular value. |
It represents cumulative probabilities, showing how probabilities accumulate as you move along the variable’s range. | It represents probabilities as a smooth curve, indicating how likely different outcomes are within a range of values. |
It provides the probability for specific values or ranges of values. | It does not give probabilities for specific values but indicates the probability density around each value. |
It is always non-decreasing, which means, as the variable’s value increases, the probability also increases or remains the same. | It can take any value within its range, but the total area under the curve must sum up to 1. |
It is used to calculate probabilities for specific events or intervals. | It is used to understand the overall distribution of probabilities for a continuous random variable. |
CDF vs. PDF: What is the Difference?
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.
Table of Content
- What is a PDF?
- What is a CDF?
- Difference Between CDF and PDF
- Relation Between PDF and CDF