Yates' theorem is a statistical principle that provides a method for estimating the variance of a sample mean when dealing with missing data. It helps researchers understand how the absence of certain data points can affect the overall analysis and conclusions drawn from a dataset. By applying this theorem, statisticians can adjust their calculations to account for the uncertainty introduced by missing values. The theorem is particularly useful in fields like psychology, sociology, and economics, where data collection can be incomplete. It allows for more accurate interpretations of results, ensuring that findings are robust and reliable despite the challenges posed by missing information.