What concept refers to the average of the squared deviation scores from the mean of a distribution?

The correct answer is variance, which is defined as the average of the squared deviation scores from the mean of a distribution. Variance measures how much the scores in a dataset spread out from the mean value.

To understand this conceptually, consider the process of calculating variance: first, you find the mean of the data set. Then, you determine how far each individual score is from that mean—these differences are known as deviation scores. Squaring these deviations eliminates any negative signs and emphasizes larger deviations. Finally, you average these squared deviations, which gives you the variance.

Variance is a critical statistic in research and program evaluation because it provides insights into the consistency or variability of the data. A high variance indicates that the data points are spread out over a wider range of values, whereas a low variance suggests that the data points are closer to the mean. Understanding variance is essential for conducting further statistical analyses, such as calculating standard deviation, which is the square root of variance and also a key measure of variability.