What does regression to the mean explain?

Regression to the mean is a statistical phenomenon that describes how extreme measurements are likely to be closer to the average when measured again. This concept applies particularly in situations where variations are affected by random factors. When an observation is significantly above or below the average, subsequent measurements are expected to gravitate towards the mean. This occurs because extreme scores often include a degree of randomness or error, and as these factors level out with repeated measurements, the scores tend to become less extreme.

In the context of the provided choices, this principle is particularly well illustrated by the concept of extreme values trending towards the average upon retesting. This means that if an individual scored exceptionally high or low on a first test, their next score would likely be closer to the mean score of the group, rather than repeating the extreme.

The other options present concepts that do not accurately represent regression to the mean. For example, suggesting that mean scores rise due to treatment effects implies a consistent upward change caused by an intervention, which is different from the idea of scores fluctuating around the mean. Similarly, the assertion that participants perform consistently across different tests negates the variability that regression to the mean aims to explain. Finally, the statement about outliers having no effect on average scores overlooks how out