In this lesson, we learn about the Central Limit Theorem and how it applies to inferential statistics. The theorem states that regardless of the distribution of the population, the distribution of all possible sample means will be approximately normally distributed if certain conditions are met, such as taking a simple random sample of size n greater than or equal to 30. Additionally, the mean of all possible sample means will equal the mean of the population, and the size of the standard deviation of the sampling mean will be the standard deviation of the population divided by the square root of the number in your sample.
Part 1 of an explanation of the Central Limit Theorem in statistics.
Produced by Kent Murdick
Instructor of Mathematics
University of South Alabama