midProbability
What is the intuition behind probability density function (PDF)?
Updated May 17, 2026
Short answer
A PDF describes relative likelihood of a continuous random variable taking values in a range.
Deep explanation
Unlike discrete probability, continuous variables cannot assign probability to a single point. Instead, PDF represents density, and probability is obtained by integrating over an interval. Higher PDF values indicate regions where data is more concentrated.
Real-world example
Height distribution in a population where exact height probability is zero but ranges matter.
Common mistakes
- Interpreting PDF value as probability.
Follow-up questions
- Why is probability at a point zero?
- What gives actual probability in continuous case?