Non-Gaussianity refers to the departure of a probability distribution from a Gaussian or normal distribution. In the context of research, non-Gaussianity can refer to deviations from Gaussian behavior in various statistical distributions, such as in the distribution of data points, errors, or noise in a dataset. This can have important implications for statistical analysis, as the assumption of Gaussianity is often used in many statistical methods. Researchers study non-Gaussianity in various fields, including finance, physics, signal processing, and machine learning, to understand the underlying properties of data and develop more accurate statistical models.