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):
two-sided test, because I'm not sure which group is better than the other. Each t-test represents one stand-alone hypothesis.