Final answer:
The concept that if something is extreme on a first measurement, it will tend to be closer to the average on a second measurement, is known as regression towards the mean, which means it's more likely to move toward the average.
Step-by-step explanation:
If something is far above or far below the average, the next example will likely be c) closer to the average. This phenomenon is known as regression towards the mean, which is a statistical concept that states if the first measurement is extreme, the second measurement will tend to be closer to the average. It does not mean that the results will actually be near the average, just that they are more likely to move toward it.
For example, consider a basketball player who scores an unusually high number of points in one game, e.g., far above his season average. According to regression towards the mean, it is far less likely than him to score as many points in the following game, and his performance is expected to regress closer to his statistical average.
When applying this concept to a box plot where the data might be skewed, if the median is very high compared to the mean, we might find that the new data points will tend to pull that mean up or down towards the median, depending on whether the outliers are high or low. The median tends to be in the middle, but generally closer to the top or base of the dataset in the presence of outliers. This observation is similar to the scenario where if outliers are high; data points are low; the mean is expected to stay the same because the outliers are accounted for in the calculation of the mean.