Prior probability refers to the initial assessment of the likelihood of an event occurring before any new evidence is taken into account. For example, if you want to know the chance of it raining tomorrow, you might start with the historical average for that day in your area. This initial estimate is your prior probability, which helps set a baseline for further analysis. When new information becomes available, such as a weather forecast predicting rain, you can update your prior probability. This process of adjusting your beliefs based on new evidence is a key concept in statistics and is often used in fields like machine learning and Bayesian inference.