It forces researchers to think twice before they make causal claims about the treatments they study. Good, because it can be a little difficult to explain. The first thing you need to realize when trying to understand regression to the mean is that testing measures are rarely perfectly reliable. Because of this, pretest scores on a measure will not correlate perfectly with posttest scores. The individual scores on the same two tests taken on differnt occasions will almost always vary.
Measurement error plays a big role in regression.
An observed score is comprised of the test-taker's true score plus the degree of measurement error. For example, on a test of reading comprehension, the true score is that person's true reading comprehension ability. The next time he or she takes the test, that error condition is likely to be different. So, while one's true ability may not change, the score on the test will be different. Well, the amout of difference that is likely to be observed the second time a person is tested is related to how far above or below the group mean that person's score was, and the degree of pretest-posttest reliability of the measure.