What is the Empirical Rule used for in statistics?

The Empirical Rule is a fundamental concept in statistics that specifically pertains to the distribution of data in a normal (Gaussian) distribution. It describes how data values are spread in relation to the mean and standard deviations. According to the Empirical Rule, approximately 68% of data values fall within one standard deviation of the mean, about 95% fall within two standard deviations, and around 99.7% fall within three standard deviations.

The focus on percentages of data values relative to standard deviations of the mean makes this answer particularly relevant for understanding the characteristics of normally distributed data. This rule is widely used in fields such as business, finance, and social sciences, as it helps to make predictions and draw conclusions about data sets.

Other options, such as calculating the median, identifying outliers, or assessing normality, do not capture the essence of what the Empirical Rule specifically addresses. The median involves a measure of central tendency, outlier detection focuses on identifying anomalies in data, and normality assessments deal with establishing whether data follow a normal distribution. While these concepts are important in statistics, they do not relate to the application of the Empirical Rule itself.