The Central Limit Theorem, Part 1 of 2
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
- What is the Central Limit Theorem in statistics?
- What has to be true to use the Central Limit Theorem?
- What does the Central Limit Theorem mean?
- Why is the Central Limit Theorem useful?
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Staff Review
This video explains very clearly and in way very understandable to most students what the Central Limit Theorem is and what it means in statistics. A good starting place for understanding some very basic and critical ideas in statistics.
