PSY30100 Lecture Notes - Lecture 6: Central Limit Theorem, Standard Deviation, Sampling Distribution
Document Summary
Samples offer a partial image of a population. This means that parts of the complete picture tend to be missing. i. e. there is usually a sampling error. Distribution of sample means a set of mx from all possible random samples of specific size (n) Sampling distribution the distribution of statistics obtained from random samples of size (n) A sample mean is not judged based on its closeness to the population mean because the selected sample may produce a high sampling error. Proximity to population mean may be caused by sheer luck. Central limit theorem for any population with mean = and standard deviation = , the sampling distribution of sample means of size (n) will have mx = and where n = sample size. As sample size (n) approaches infinity, the sampling distribution will become a normal distribution. Sample distribution is normal regardless of sample size (n) Sample distribution is only normal if sample size (n) approaches infinity.
