What is the tendency for extreme scores to regress toward the mean known as?

The phenomenon where extreme scores tend to move closer to the mean upon subsequent measurements is known as regression toward the mean. This concept is based on the idea that extreme measurements are often influenced by random variations. When repeated measurements occur, these extreme scores are likely to be less extreme due to these random fluctuations, resulting in a return toward the average or mean.

This is particularly important in statistical analysis and experimental research, where researchers need to recognize that extreme observations may not be representative of typical outcomes. Instead, they are simply outliers impacted by various factors, and when samples are taken again, the results will likely fall closer to the mean value. Understanding this concept helps avoid incorrect interpretations of data based solely on extreme measurements.

The other choices reference different statistical concepts. Standard deviation measures the amount of variation or dispersion in a set of values, the outlier effect pertains to the influence of extreme data points on the overall analysis, and average adjustment does not have a specific meaning in the context of regression analysis.