SOC222H5 Lecture Notes - Lecture 4: Central Limit Theorem, Statistical Inference, Sampling Distribution
Document Summary
Every application of inferential statistics involves 3 different distributions. Information from the sample is linked to the population via the sampling distribution. Every unit or case in the population has an equal chance of being selected; Selection of any single case or unit can in no way affect the selection of any other unit or case; All combinations of cases or units are possible. Mean, median, and mode coincide in the middle position. Suppose we know the population mean is 75. Has a mean equal to the population mean. Has a standard deviation (standard error) equal to the population standard deviation divided by the square root of n. For any trait or variable, even those that are not normally distributed in the population, as sample size grows larger, the sampling distribution of sample means will become normal in shape. The importance of the central limit theorem is that it removes the constraint of normality in the population.
