Statistics

Central Limit Theorem – Sampling Distribution of Sample Means – Stats & Probability

This statistics video tutorial provides a basic introduction into the central limit theorem. It explains that a sampling distribution of sample means will form the shape of a normal distribution regardless of the shape of the population distribution if a large enough sample is taken from the population. This video gives plenty of examples and practice problems.

Here is a list of topics:
0:00 – An Introduction To The Central Limit Theorem
2:55 – The Sampling Distribution of the Sample Mean
5:52 – Basic Review of Statistical Symbols
7:57 – The Law of Large Numbers
11:19 – The Z-Score Formula For Sampling Distributions
12:54 – The Relationship Between Sample Size and Standard Error
15:31 – The Uniform Distribution Review
17:50 – The Exponential Distribution Review
20:25 – The Normal Distribution vs The Sampling Distribution
23:58 – Probability Problems
32:33 – How To Find The 80th Percentile of a Sampling Distribution
34:43 – Probability Problems With Uniform Distribution & Sampling Distribution of the Sample Mean
46:06 – Sampling Distribution of Sample Sum
51:18 – Probability Problems With Exponential Distribution
55:26 – Finding The IQR of a Sampling Distribution