ABSTRACT

Methods in inferential statistics are used to draw conclusions about a population and to measure the reliability of these conclusions using information obtained from a random sample. Inferential statistics involves techniques such as estimating population parameters using point estimates, calculating confidence interval estimates for parameters, hypothesis testing, and modeling (e.g., regression and density estimation). To measure the reliability of the inferences that are made, the statistician must understand the distribution of any statistics that are used in the analysis. In situations where we use a wellunderstood statistic, such as the sample mean, this is easily done analytically. However, in many applications, we do not want to be limited to using such simple statistics or to making simplifying assumptions. The goal of this chapter is to explain how simulation or Monte Carlo methods can be used to make inferences when the traditional or analytical statistical methods fail.