Which of the following data-classification methods selects class break levels by taking into account statistics such as the mean of the values and the average distance that values are away from the mean?

The method of data classification that selects class breaks based on statistics such as the mean of the values and the average distance that values are from the mean is the Standard Deviation method. This method is particularly useful for normally distributed data. It works by computing the mean and standard deviation of the dataset and then creating class breaks across intervals based on these statistics.

For instance, one might create classes that represent one standard deviation above and below the mean, thus helping to illustrate how values are dispersed relative to the average. This means that data points falling within one standard deviation from the mean can be classified together, making it easier to visualize and analyze distributions, particularly in identifying outliers or general patterns in the data.

Understanding the distribution of data alongside the mean and standard deviation allows for a more refined classification that reflects the actual variation within the dataset rather than simply dividing data into equal ranges, which is what methods like Equal Interval employ.