Fisher Information is a concept in statistics that measures the amount of information that an observable random variable carries about an unknown parameter of a statistical model. It quantifies how much the likelihood function changes as the parameter changes, indicating how well we can estimate that parameter based on the data. In practical terms, higher Fisher Information means that the data provides more precise information about the parameter, leading to better estimates. It is often used in the context of maximum likelihood estimation and plays a crucial role in statistical inference and information theory.