What does standard deviation measure in a dataset?

Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data points. Specifically, it indicates how much individual scores in a dataset deviate from the mean (average) value of the dataset. A low standard deviation suggests that the scores tend to be close to the mean, while a high standard deviation indicates that the scores are spread out over a wider range of values. This characteristic makes standard deviation an essential tool for understanding the distribution of data points, especially in contexts where identifying variability is crucial, such as in finance, psychology, and quality control.

In contrast, the other options refer to different statistical concepts. The central tendency of scores pertains to measures like mean, median, or mode, which focus on the 'typical' value in the dataset rather than its variability. The frequency of occurrence relates to how often each score appears, which is not reflected in standard deviation. Lastly, the total number of scores is merely a count and doesn't provide any insight into how those scores are distributed or how they relate to one another. Thus, the correct choice accurately captures the essence of what standard deviation measures regarding data variability.