Central differences refer to the method of calculating the difference between values at the center of a dataset. This approach is often used in statistics to analyze the variation or change in data points, providing insights into trends and patterns. By focusing on central values, researchers can better understand the overall behavior of the dataset. In the context of numerical analysis, central differences are commonly used in finite difference methods to approximate derivatives. This technique involves taking the average of the differences between adjacent points, which helps in solving differential equations and modeling physical phenomena, such as those described by Newton's laws or fluid dynamics.