You obviously have no idea what you're talking about if you think one basic stats course at the med student level covers what you need to understand clinical research. Some examples of essential basic epidemiological concepts you may not know the answer to:
1) When is it appropriate to adjust for covariates? If a covariate is related to the outcome but not the variable of interest, should we put it in the model? What exactly qualifies as a confounder? If something is a confounder, should we stratify by it and make a subgroup or control for it, and what's the difference? Do we include confounders in RCTs if the design by nature eliminates confounding?
2) How about this real example?
To investigate the association between estrogen and cancer, Yale investigators considered the possibility of ascertainment bias, where estrogens lead to vaginal bleeding that accelerates the diagnosis of existing cancer (more likely to detect it if bleeding occurs). Therefore, they stated that we can look at only vaginal bleeding cases whether they are taking estrogen or not; if these patients all have bled, they must have the same likelihood of being diagnosed with cancer. If estrogen still leads to cancer among these patients, they stated that we can say it's causal. What was the serious flaw in this methodology? (Why do we find an association between estrogen and cancer even among women who bleed, if there is no real association?) How about in Belizan et al 1997? (Hint: similar concept as the prior)
Belizán JM, Villar J, Bergel E, et al. Long-term effect of calcium supplementation during pregnancy on the blood pressure of offspring: follow up of a randomised controlled trial. BMJ. 1997;315(7103):281-285.
3) What assumptions are made with Cox regression? What if proportionality assumptions are not met (very common); how should this be interpreted? What should be used as the time scale for your Cox regression, and how does this affect the analysis? (age, follow-up time, etc)
I can provide countless other examples...even in this thread, the p-value and 95% CI are not synonymous and corresponding concepts as the above poster alluded. These are not obscure topics that will never pop up in real life. They are the bare essentials to any clinical researcher that are guaranteed to come up (but many physicians ignore them unknowingly) and anyone who has taken real graduate level introductory epidemiology classes would know the answers. This lack of fundamental understanding is one of the main reasons why so many observational studies fail to stand up to the rigor of clinical trials. With proper design, cohort studies can closely mirror the results of RCTs, and case control studies should yield effect estimates as valid as cohort studies. However, that is rarely the case in reality.