What is typically measured when evaluating the standard deviation in a dataset?

The measurement of standard deviation in a dataset specifically focuses on the spread of data values around the mean. Standard deviation quantifies how much individual data points differ from the mean of the dataset. A low standard deviation indicates that the data points tend to be close to the mean, while a high standard deviation suggests that the data points are spread out over a wider range of values. This metric is crucial for understanding the variability in a dataset, which can inform analyses and interpretations in different contexts, such as identifying consistency in data or assessing risk in statistical models.

In contrast, options related to the average score, total number of data points, or the presence of outliers either reference different aspects of data analysis or concepts that do not directly pertain to the calculation of standard deviation. Thus, they do not accurately capture what is being measured by standard deviation.