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Regression To The Mean in Psychology: Definition & Example

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Lesson Transcript
Instructor: Yolanda Williams

Yolanda has taught college Psychology and Ethics, and has a doctorate of philosophy in counselor education and supervision.

Regression to the mean occurs when the second measurements of a particular variable are less extreme than the first. Learn about regression to the mean, random variance, and more.

Definition

Regression to the mean is a statistical phenomenon stating that data that is extremely higher or lower than the mean will likely be closer to the mean if it is measured a second time. This means that if you take two independent sets of measurements from every person in your sample, you would find that the people who had scores that were well above or below the mean during the first measurement would have scores that are closer to the mean in the second measurement.

Regression to the mean, also known as regression toward the mean, was discovered by Sir Francis Galton while he was conducting reporting the heights of 250 parents and their 930 children. Galton calculated the average height for the adults and children and plotted the heights of everyone on a chart. Galton found that parents who were taller than average tended to have children that were taller than average, and the parents who were shorter than average tended to have children who were shorter on average.

However, in instances where the parents were taller than average, the children tended to be a bit shorter than the parents, and in instances where the parents were shorter than average, the children tended to be a bit taller than the parents. In other words, the children of parents with heights that were extremely above or below average had heights that were closer to the average. Galton called this phenomenon regression of the mean.

Regression to the mean is due to random variance, or chance, which affects the sample. For example, part of height is due to our genes that we inherit from our parents, but there are also other random influences that may affect your height. It is the random variance that causes some of the samples to have extreme values. It is important to note that the random variance in the second measurement is not influenced by the random variance that affected the first measurement. Because of this, the samples will appear to regress on the second measurement.

Example

Suppose that we were interested in studying the level of aggression in football players after losing a game. You took a sample of 50 football players and measured their level of aggression after they lost to another team during home game. You record the data into a chart and find that the average aggression score is 72 (out of 100). The scores of the 50 players range from 41 to 100.

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