Introduction
A continuous random variable (CRV) can take any value in an interval — for example, the exact height of a randomly chosen student, or the time until a component fails. Unlike a discrete random variable, we cannot list individual probabilities for each outcome; instead, probability is described by a probability density function (PDF), which spreads probability continuously across an interval.
In the 9709 Paper 6 exam, questions on PDFs regularly ask you to verify that a function is a valid PDF, find unknown constants, and calculate probabilities as definite integrals. Mastering this topic is therefore essential for confident performance in PS2.
Core Concept
Continuous Random Variables
A continuous random variable takes values over an interval (which may be infinite). Individual point probabilities are always zero: for any specific value . Probability is only meaningful over an interval.
The Probability Density Function
A function is a valid probability density function for a CRV if and only if it satisfies two conditions:
- Non-negativity: for all in the domain (and outside the domain).
- Total area equals 1: The integral of over the entire domain equals 1.
Probabilities are computed as areas under the curve:
Because , it makes no difference whether the endpoints are included:
Infinite Domains
The syllabus explicitly includes PDFs defined on infinite intervals, such as for . In these cases, the total-area condition involves an improper integral:
provided the limit exists and equals 1.
Unlock the full Continuous Random Variables note with Nova
You're reading the preview. Unlock the complete note — every worked example, examiner pitfall and practice question — plus 24/7 AI tutoring from Nova that teaches directly from these notes.