n=120 pales utterly beside the observation that it is totally unclear what they are actually manipulating or measuring. The design is completely hapless.
If you really need to detect effects that are so small they are meaningless for almost any practical purpose, then you might need thousands of people in the study.
Just because you think a study seems too small doesn't mean it is scientifically invalid. There are actually statistical tests you can use just for the specific purpose of determining validity. These are much better than some intuitive hunch you might have about the study.
Xcelerate is correct. In most studies of any size (in my experience) you start by guessing the effect size that you think you might see. You then try to design your study with a certain power to detect an effect of that size.
You can try to manipulate your power in a variety of ways through the sample selection process (which can ultimately modify your external validity). The major variable that most people manipulate is simply the size of the sample (i.e., the number of people). You need a sample of a large enough size such that you could even hope to detect the effect you expect to see.
Ultimately, one can say, "This study had 80% power to detect an effect size of 30%."
If you really need to detect effects that are so small they are meaningless for almost any practical purpose, then you might need thousands of people in the study.