What does regression to the mean imply about extreme measurements?

Regression to the mean is a statistical concept that describes the phenomenon where extreme values on one measurement tend to be closer to the average (or mean) upon retesting or measurement. This means that if an extreme measurement is observed, the subsequent measurements are likely to be less extreme and more representative of the overall average.

This occurs because extreme measurements can often be influenced by random variation or external factors. When those influences are removed or minimized in later measurements, the results are expected to reflect the true mean of the distribution rather than the outlier. Thus, option A accurately captures this principle, indicating that subsequent measurements are likely to be less extreme than the initial measurement, reinforcing the idea of regression to the mean.

The other choices do not accurately align with the concept. For instance, option B incorrectly suggests that subsequent measurements will always be more extreme, which contradicts the essence of regression to the mean. Option C erroneously states that extreme measurements do not affect the mean, while they can, in a sample context, show variability that influences measures of central tendency. Lastly, option D implies that extreme measurements are reliable indicators of trends, which is misleading, as they may simply represent outliers rather than consistent patterns over time.