Heavy-tailed distributions are statistical distributions that have tails that are significantly heavier than those of a normal distribution. This means that they have a higher probability of producing extreme values or outliers. Common examples of heavy-tailed distributions include the Pareto distribution and the Cauchy distribution. These distributions are often used in fields like finance and telecommunications to model phenomena where rare events have a substantial impact. In heavy-tailed distributions, the probability of observing very large values decreases slowly compared to lighter-tailed distributions. This characteristic can lead to unexpected risks and challenges in predicting outcomes, making them important for understanding real-world scenarios such as natural disasters or market crashes. Recognizing heavy-tailed behavior helps in better risk management and decision-making.