EC255 Lecture Notes - Lecture 9: Standard Deviation, Random Variable, Stratified Sampling
Document Summary
Ec255 lecture #9: sampling & sampling distributions. Learning objectives: determine when to use sampling instead of a census, distinguish between random and non-random sampling, understand the impact of the central limit theorem on statistical analysis, use the sampling distributions. Reasons for taking a census: eliminate the possibility that by chance a random sample may not be representative of the population for the safety of the consumer. Reasons for sampling: sampling can save money and time it can broaden the scope of the data set research process is sometimes destructive, the sample can save product if accessing the population if impossible. Descriptive vs inferential statistics inferential statistics: using data obtained through sampling to estimate the value of or test a hypothesis about a parameter (inductive logic) transform information into knowledge. Sampling distribution: a sampling distribution is a distribution of the possible values of a statistic for a given size sample selected from a population.
