Toprak Tuğba Elif2023-03-222023-03-2220199781351741231https://doi.org/10.4324/9781315187815-9https://hdl.handle.net/20.500.14034/903Language testers may need to examine the differences between two or more groups as a result of differing conditions and determine whether these differences are meaningful. Since there could be variability when measuring constructs, researchers use statistical techniques to determine whether the variability extends beyond differences among participants and measurement error. There are two statistical techniques that could be employed to examine such relationships: the t-test for examining the differences between two groups and analysis of variance (ANOVA) for capturing the differences between more than two groups. This chapter provides an introduction to the t-test, ANOVA, their special cases, and non-parametric equivalents. Moreover, it illustrates how these techniques can be used by reporting on two studies conducted in an operational setting. The first study investigates the impact of an alternative treatment for listening and pronunciation on examinees’ test performances by using the Mann-Whitney U test, the non-parametric equivalent of the t-test. The second study utilizes ANOVA procedures to examine meaningful differences between the performances of four groups of examinees on a reading test. © 2019 selection and editorial matter, Vahid Aryadoust and Michelle Raquel.eninfo:eu-repo/semantics/closedAccessBook Chapter10.4324/9781315187815-91791972-s2.0-85128042277N/A