Continuous distributions are a type of probability distribution where the outcomes can take on any value within a given range. Unlike discrete distributions, which deal with distinct and separate values, continuous distributions represent data that can vary smoothly. For example, the height of individuals or the time it takes to complete a task can be modeled using continuous distributions, as these measurements can fall anywhere within a range. One common example of a continuous distribution is the normal distribution, often depicted as a bell curve. This distribution is important in statistics because many natural phenomena, such as test scores or measurement errors, tend to follow this pattern. Understanding continuous distributions helps in making predictions and analyzing data effectively.