I dont understand the comments of the reviewers at all. A t test is a statistical test, commonly used in statistical analysis (including ANOVA and regression). A f test shows the overall strength of the model, if you have only one independent variable it should yield exactly the same result as a t test (or at least it does in regression). I dont understand how you could have interaction in your model, that requires multiple independent variables which I dont see mentioned.
The ANOVA F statistic will yield the same substantive results as a independent t test as long as only two levels are being compared. As you increase the number of levels being compared (say you are comparing how three levels of an IV influence two levels of a DV) then the independent t test will generate family wise error, the ANOVA test addresses this and should not.
I would think that a t test would be fine if you have enough data (high enough sample size) and its normally distributed and you can calculate a mean.