[EC285] - Midterm Exam Guide - Ultimate 37 pages long Study Guide!
Document Summary
Population is the entire population and a sample is a smaller group of individuals. When a sample is biased, the summary characteristics of a sample differ from the corresponding characteristics of the population it is trying to represent. A parameter is a descriptive measure of population and statistics if a descriptive measure of a sample. Sample statistic estimates the corresponding parameter of the population: if a sample does this, it is said to be representative. Randomization can protect against factors that you aren"t aware of. Sampling error is the discrepancy between population and sample because sample is random and only a part of population. Eliminated when sample = population: higher the sample size, smaller chance of having a sampling error. Non-sampling error is the discrepancy between population and sample that is caused by a systematic factor (not random) Leads to a biased estimate: statistic is systematically higher or lower than the parameter. Cannot be solved by increasing sample size.
