Statistical inference using hypothesis-testing methods

This article is envisioned to form a base uponwhich a full-blown exhaustive discussion ofhypothesis-testing could take place.Statistical inference is the process of generating conclusions or generalizations about certain characteristics of the population on the basis of information obtained from a sample. Statistical inference can be conducted in two ways: by interval estimation of parameters and by testing the statistical hypothesis on parameters. These are actually reverse processes much like addition and subtraction or multiplication and division in arithmetic or differentiation and integration in calculus. The following discussion is limited to the hypothesis-testing procedures applied to a selected batch of parameters such as (1) the population mean, µ, and (2) the population proportion, π, for one-sample tests of significance; (3) the difference of two population means, µ 1- µ 2, for independent samples and (4) the difference of two population proportions, π1- π2, for two-sample tests. A statistical hypothesis is a statement about the numerical value of a population parameter. There are six steps involved in the hypothesis-testing procedure, as shown in Figure 1. Step 1. State the null hypothesis, Ho The null hypothesis is usually the hypothesis that the re-searcher wants to gather evidence against or is the hy-pothesis to be tested. The Ho carries the symbol = ; or a