Stats: Central Limit Theorem

Sampling Distribution of the Sample Means

Instead of working with individual scores, statisticians often work with means. What happens is that several samples are taken, the mean is computed for each sample, and then the means are used as the data, rather than individual scores being used. The sample is a sampling distribution of the sample means.

When all of the possible sample means are computed, then the following properties are true:

The formula for a z-score when working with the sample means is:

Finite Population Correction Factor

If the sample size is more than 5% of the population size and the sampling is done without replacement, then a correction needs to be made to the standard error of the means.

In the following, N is the population size and n is the sample size. The adjustment is to multiply the standard error by the square root of the quotient of the difference between the population and sample sizes and one less than the population size.

For the most part, we will be ignoring this in class.

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