 Chapman–Robbins bound

The Chapman–Robbins bound is a result in statistical theory that provides a method for creating confidence intervals for the mean of a distribution based on a sample. Specifically, it is often applied in the context of estimating the mean of a bounded distribution, particularly when we have limited information about the distribution's shape. The bound addresses the problem of how many observations are needed to ensure that the estimated mean lies within a specified error margin with a certain probability.