I have conducted an experimental study, with 1 within-variable (time: T1 and T2) and 1 between-variable (group: control and treatment), measuring just one dependent variable.
I understand that this is a design which would require a mixed ANOVA analysis. Of course two distinct t-tests would be much easier: one dependent and one independent two-sample-t-tests.
What is problematic about using two distinct t-tests in comparison to mixed ANOVA (despite ignoring the interaction effect, which I assume to not exist)?
Thanks for advice.
Update: What I've done so far (in R) is:
t.test(Con$DELTA, mu=0, alternative = c("greater"))
t.test(Exp$DELTA, mu=0, alternative = c("greater"))
Two single dependent/paired t.tests for each group, one-sided, because I'm just interested in each groups behavior change success. Afterwards I can compare both groups (with independent two-sample-t-test):
t.test(Con$DELTA, Exp$DELTA)
two-sided test, because I'm not sure which group is better than the other. Each t-test represents one stand-alone hypothesis.
Best Answer
A practical situation in which the t-test approach is inferior to mixed models in this simple setting is when you have missing data. In this case, the t-test approach is only valid under the missing completely at random assumption, whereas the mixed models are valid under both the missing completely at random and the missing at random assumptions.