Zuckerman's Bound is a concept in the field of information theory and statistics, specifically related to the estimation of parameters in statistical models. It provides a limit on the amount of information that can be gained from a sample when estimating a parameter, ensuring that the estimates remain reliable and valid. The bound is named after David Zuckerman, who contributed to the understanding of how much information can be extracted from data. It emphasizes the importance of sample size and the quality of data in making accurate predictions and inferences in various applications, including machine learning and data analysis.