In a hypothetical study on EFL learning, I want to examine whether the use of AI writing tools improves students’ essay scores. I divide participants into two groups: one ...
In a hypothetical study on EFL learning, I want to examine whether the use of AI writing tools improves students’ essay scores. I divide participants into two groups: one group uses AI tools during writing tasks, while the other group does not. Each group consists of different students, and no participant appears in both groups.
Because the data come from two independent groups of participants, this scenario calls for an independent samples t-test. The purpose is to compare the mean essay scores between the two groups to determine whether the difference is statistically significant. Since the groups are unrelated and there is no pairing or matching between individuals, an independent test is the appropriate choice.
In contrast, if I had measured the same group of students before and after using AI tools (e.g., comparing their essay scores in a pre-test and a post-test), then a paired samples t-test would be more suitable. This is because the data would be dependent, with each participant contributing two related scores.
The key reasoning behind the choice of test lies in the structure of the data: independent samples t-tests are used when comparing different groups, while paired samples t-tests are used when comparing the same participants across two conditions or time points.