continuous random variable

In statistics, a continuous random variable can take on an infinite number of values, unlike a discrete variable with a finite number of outcomes. The probabilities for a continuous random variable are summarized by a probability density function (PDF).1

The PDF is a function whose graph is a curve above the horizontal axis. The total area under this curve is 1. The area between any two values on the curve represents the probability that the random variable will fall within that range. For example, the normal distribution is described by a bell curve, which is a type of PDF. Probabilities for specific outcomes are found by integrating the PDF over an interval corresponding to that outcome.21