The best way to describe a t test is “we first computed the standard error, which measures the differences between two samples means that is reasonable to expect if there is no treatment effect (Gravetter et al., 2021). For ANOVA “ we want to compare difference among two or more sample means” (Gravetter et al., 2021). This can best be defined as a t test can only truly be useful when there are two variables involved as opposed to an ANOVA which essentially does that same thing by finding the mean differences. The ANOVA, however, have the capability of using more then two variables in their study making is much more convenient when it comes to one’s research experiment. An example of an ANOVA study would be to find which glaucoma medication worked the best in patients. The ANOVA study would be able to look at three different types of medication effectively regardless of the results. A t test would only be able to look at two different but essentially, they both would be able to determine the mean of the study.