Statistical theorems are fundamental principles in statistics that provide a framework for understanding data and making inferences. They often describe the behavior of statistical measures, such as means and variances, under certain conditions. Examples include the Central Limit Theorem, which states that the distribution of sample means approaches a normal distribution as sample size increases, regardless of the original data distribution. These theorems are essential for hypothesis testing and confidence interval estimation, helping researchers draw conclusions from sample data. They rely on mathematical proofs and assumptions, ensuring that statistical methods yield reliable results. Understanding these theorems is crucial for anyone working with data in fields like economics, psychology, or health sciences.