Beta, or Type II error is the risk of a false negative. Power is 1-beta, the probability that the experiment will be able to detect an effect that is really there. Type II or Beta Error Truth Play with some p values for beta in your head, for betas and alphas for, say, a treatment that doesn't hurt a person a lot, for a treatment that's very dangerous, for a disease that's devastating, for a disease that doesn't cause much harm. Then decide what kinds of experiments you would design and what kinds of errors you would have with regard to those. Now, lets review a few key pitfalls that you want to make sure you avoid as potentially both a consumer and a producer of medical research.
Effects can be statistically significant and clinically inconsequential.
Beware the sweeping generalization. Results of any study only apply to populations studied in the study's samples. In fact, you can toss out a lot of the literature, because it doesn't apply to your patient population for one reason or another. Ignore reading it because it doesn't apply to where you're practicing medicine with your patient clientel. Results of any study apply only to populations similar to the study sample.