What does the term "regression toward the mean" refer to?

The term "regression toward the mean" describes the phenomenon where extreme measurements are likely to be closer to the average when measured again. This concept is based on the idea that when a variable is subjected to some random variation, extreme values are often influenced by chance factors that are not present during repeated measurements. As a result, when these extreme cases are retested, the results tend to move closer to the average, or mean, of the data.

This statistical principle highlights that performance or measurements that are exceptionally high or low are likely to return closer to the average upon subsequent observations, often due to the reduction of random error or the stabilizing effects of variability. This phenomenon plays an important role in various fields, including healthcare, where it helps to interpret the results of therapies or interventions over time, helping healthcare professionals understand that not all changes imply a real or lasting effect.

The other choices do not correctly define regression toward the mean. The average value of each variable in a model does not capture the essence of this concept. Normalizing variable scales is a preprocessing step in data analysis that has no direct relation to the idea of returning to an average. Excluding outliers may influence regression analysis, but it does not embody the notion of extreme cases regressing