ABSTRACT
Parameter estimation is used for the purpose of making population inferences—inferences about the value of some unknown population characteristic given limited information about the population. The theory of estimation is both elegant and central to statistics. The discrepancy between the readers report and the truth can be attributed to nothing other than random sampling error. Random sampling error is error in the estimation of a parameter attributable just to the random selection process—the fact that the readers sample included certain members of the population rather than others merely. For bootstrapping to be an effective method of generating a confidence interval, the readers must have some faith in their sample as adequately representing the population from which the sample was derived. Many of the problems that communication researchers face involve the estimation of a parameter such as a population mean or a proportion from a sample from the population. The sampling distribution plays a fundamental role in theory of statistical inference.
