Tests in the null hypothesis significance testing (nhst) framework are designed to identify differences between groups. Thus, researchers wishing to assess similarities are out of luck if they use widespread techniques in the field, such as t-tests, chi-square tests and linear regression. This is unfortunate given that researchers with many common study designs would benefit from knowing whether there is no difference – or at least no practical difference – between groups. For example, we might want to test empirically claims about there being no differences (regarding use of some linguistic feature) between first-language and advanced second-language English students. Or we might want to decide which sub-groups in our corpus to merge into one. This paper starts by outlining challenges inherent to the nhst framework in this context, and then provides two alternatives: equivalence testing and model-based techniques. We provide a guided introduction to both approaches for researchers wishing to expand their toolbox to carry out tests of group similarities.
Larsson et al. (Wed,) studied this question.