Continuous distribution refers to a type of probability distribution that describes the likelihood of a continuous random variable taking on a range of values. Unlike discrete distributions, which deal with distinct and separate values, continuous distributions can assume an infinite number of values within a given interval. The probability of a continuous random variable taking on any specific value is always zero; instead, probabilities are determined over intervals. Common examples include the normal distribution, uniform distribution, and exponential distribution. Understanding continuous distributions is essential in fields like statistics, machine learning, and data analysis, as they model real-world phenomena effectively.