SOC201H1 Lecture Notes - Central Limit Theorem, Statistic, Sampling Distribution
Document Summary
Sampling distributions allow us to refine the estimates provided by statistics calculated on a sample. Sampling error: difference between the calculated value of a sample statistic and the true value of a population parameter. Point estimate: a statistic provided without indicating a range of error. There is a variability in statistical outcomes from sample to sample. Repeated sampling: drawing a sample and computing its statistics and then drawing a second sample, a third, a fourth, and so on. The resulting sample means were similar in value and tended to cluster around a particular value. Sampling error is patterned and systematic and therefore is predictable: probability theorists suspected that this value was the true value of the population parameter, sampling variability was mathematically predictable from probability curves. Sampling distribution: from repeated sampling, a mathematical description of all possible sampling outcomes and the probability of each one: eg. The mean age of the population of all doctors is 48 years.
