EC255 Lecture Notes - Lecture 9: Simple Random Sample, Statistical Inference, Nonprobability Sampling
Document Summary
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 (i. e. testing a plane/car, you must check each one to ensure safety) Inferential statistics: the use of data gathered from a sample and used to reach conclusions about the population from which the sample was taken: transform information into knowledge. Simple random sample (i. e. picking random numbers from a list) Systematic random sample: cluster (or area) sampling. Errors: data from nonrandom samples are not appropriate for analysis by inferential statistical methods, the expected sampling error decreases as the sample size increases. Sampling distribution: a sampling distribution of the possible values of a statistic for a given sample selected from a population. Developing a sampling distribution: assume there is a population, population size n = 4, random variable, x, is age of individuals, values of x: 18, 20, 22, 24 (years)
