01:960:285 Lecture Notes - Lecture 16: Central Limit Theorem, Standard Deviation, Sampling Distribution

• School: Rutgers University
• Department: Statistics and Biostatistic
• Course: 01:960:285
• Professor: Shyam Moondra

Document Summary

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 mean of the sample means will be the mean of the population. The variance of the sample means will be the variance of the population divided by the sample size. If the population has a normal distribution, then the sample means will have a normal distribution. If the population is not normally distributed, but the sample size is sufficiently large, then the sample means will have an approximately normal distribution. Some books define sufficiently large as at least 30 and others as at least 31.