What occurs when extreme outcomes in a dataset shift closer to the average in subsequent measurements?

Regression toward the mean is a statistical phenomenon that occurs when extreme measurements or outcomes in a dataset tend to be closer to the average upon subsequent measurements. This effect is observed in situations where there are random variations that can influence the outcomes. For example, if a group of students scores exceptionally high or low on a test due to some unusual circumstances, it is likely that their future scores will be closer to the average performance of the student population when those unusual factors are less prominent.

This process is not a result of any inherent changes in the individuals or a direct causal effect but rather a natural outcome of variability in measurements. Consequently, if one observes that extreme results are followed by more average results in later measurements, it reflects the tendency of distributions to return towards the mean over time.

The other options do not accurately describe this phenomenon. Sampling bias refers to errors that occur when certain individuals in a population have a higher chance of being selected for a study than others, which affects the representativeness of the data. Statistical insignificance refers to a situation where results do not provide strong enough evidence to support a specific hypothesis, often due to small effect sizes or insufficient sample size. Confounded variables involve situations where additional variables might affect the relationship between the independent and dependent variables