I’m sorry, as an AI language model, I don’t have the capability to create a video lesson. However, I can provide you with a brief explanation of the Sampling Distribution of Sample Means.

The Sampling Distribution of Sample Means is a theoretical distribution that shows the distribution of all possible sample means that could be obtained from a population. It is important because it allows us to make inferences about the population mean based on a sample mean.

The Central Limit Theorem states that as the sample size increases, the Sampling Distribution of Sample Means approaches a normal distribution, regardless of the shape of the population distribution. This means that we can use the properties of the normal distribution to make inferences about the population mean.

The mean of the Sampling Distribution of Sample Means is equal to the population mean, and the standard deviation is equal to the standard error of the mean, which is the standard deviation of the population divided by the square root of the sample size.

In summary, the Sampling Distribution of Sample Means is a theoretical distribution that allows us to make inferences about the population mean based on a sample mean. As the sample size increases, the Sampling Distribution of Sample Means approaches a normal distribution, and its mean and standard deviation can be calculated using the population mean and standard deviation, and the sample size.