PSY 295 Lecture Notes - Lecture 8: Central Limit Theorem, Standard Deviation, Sampling Distribution
Document Summary
Day 11 notes: probability & sampling distributions part 2. Sampling distribution: a distribution of statistics obtained by selecting all of the possible samples of a specific size, n, from a population. The most typical is the sampling distribution of m. But you can get it for any statistic. E. g. , the sampling distribution of s (standard deviation) We"re doing all of this because we want to convert a sample mean m into a z- score and that z-score into a probability. To get a z-score, we need to know the mean and the standard deviation of the distribution of sample means. The mean of the sampling distribution of the mean. Approaches normal distribution as n gets larger. The standard deviation of the sampling distribution of the mean. Can be predicted precisely if you know and n. /sqrt(n) and will approach a normal distribution as n approaches infinity.
