Students are guided through learning about comparing the means of two groups / levels using a t-test. The differences between a paired samples t-test and an independent samples t-test are reviewed along with the statistics’ assumptions. When two independent groups do not have equal variances, students are coached through completing a Mann–Whitney U test. Students are also guided through creating charts that can accompany their results in SPSS or R.

This chapter explores commonly used nonparametric tests in applied linguistics, focusing on their assumptions research, applications, and result interpretation. Nonparametric tests are crucial for analyzing data that do not meet parametric test assumptions, making them valuable in various research scenarios. The chapter explains nonparametric tests and their significance, especially when data distribution is unknown or sample sizes are small. Key tests include the Mann–Whitney U test for comparing two independent groups, the Wilcoxon signed-rank test for paired samples, the Kruskal–Wallis test for comparing more than two independent groups, and the Friedman test for related samples. The chapter also discusses the limitations of nonparametric tests, such as reduced power compared to parametric tests. Hypothetical examples relevant to applied linguistics research are provided, along with step-by-step instructions for conducting these tests in SPSS. By the end of the chapter, you will be able to effectively use nonparametric tests in your research.