Statistical Principles

Abstract

The measurement and the analysis of random data involve uncertainties and estimation errors that must be evaluated by statistical techniques. This chapter reviews and illustrates various statistical ideas that have wide applications to commonly occurring data evaluation problems. The most important probability distribution function from the viewpoint of applied statistics is the Gaussian (normal) distribution. There are three other distribution functions associated with normally distributed random variables that have wide applications as statistical tools. These are the ?2 distribution, t distribution, and F distribution. A more meaningful procedure for estimating parameters of random variables involves the estimation of an interval, as opposed to a single point value, which will include the parameter being estimated with a known degree of uncertainty. The chapter outlines the simplest form of a statistical procedure called hypothesis testing. It provides a brief review of correlation and regression concepts from the viewpoint of elementary statistics.Controlled Vocabulary Termschi-squared distribution; confidence interval; Gaussian Distribution; hypothesis testing; probability distribution; sample variance; sampling distribution