ABSTRACT
Having examined statistical analysis involved with one variable, people turn to what to do with two or more variables. Statistical comparison of two variables is called bivariate analysis. This chapter introduces some key concepts in testing for statistical significance, provides an overview of the tests from which people can choose and discusses in more detail the logic behind the more common tests. The credibility of a research project will stand or fall on the selection of the most appropriate statistical test. There are two main types of statistical test: parametric and non-parametric. Parametric tests are more powerful. They require that a random sample be drawn, that the variables are normally distributed within the population, and that at least one variable is at the interval or ratio level of measurement. Non-parametric tests are generally less powerful than parametric tests, but can be used in studies where the conditions for parametric tests cannot be met.
