Which term best describes the average score subtracted by one standard deviation?

The term that best describes the average score subtracted by one standard deviation is standard deviation itself. Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values. When you subtract one standard deviation from the mean, you are essentially identifying a specific point in the distribution where approximately 68% of the data falls under a normal curve (between one standard deviation below and one standard deviation above the mean).

This concept is integral in understanding how individual scores relate to the average score in a set. By determining the score at one standard deviation below the mean, it gives insight into how much lower than the average a score can be while still being considered within the range of typical data points.

The other options relate to different statistical concepts: variance measures the spread of data points around the mean but does not denote a specific score. The mode is the value that appears most frequently in a dataset, and the lower quartile indicates the median of the lower half of a dataset. None of these terms accurately reflect the action of subtracting one standard deviation from the average score, which is specifically associated with the standard deviation itself.