It's always frustrating for laypersons who are attempting to educate themselves when studies on similar topics yield somewhat different results. When reading research, it is very common to come across the issues of sample size versus statistical significance. Since people vary so much, it is always necessary to have multiple patients in each group of a study. That way, if you get a result, you know it applies to people in general, and not just a couple of unusual people that you happened to study. Results comparing one group to another can usually be described in terms of effect size.
Effect sizes can be small or large.
When you are looking at small effect sizes, the number of people you need in your study groups goes up. That's because it can be difficult to distinguish a true small effect size from the natural random variation that occurs from person to person - if you have a large number of people, however, and an effect persists as you look at more and more people, the researcher can conclude with confidence that the small effects does indeed exist. The current researchers are caught in the middle of this process. They have enough people to suspect that there is an effect, but they do not have enough patients in order to prove that effect to the satisfaction of the scientific community .