What term describes the number of standard deviations a data point is from the mean?

The term that describes the number of standard deviations a data point is from the mean is known as the standard score. The standard score, often referred to as a z-score, quantifies how far away, in terms of standard deviations, a particular data point is from the mean of the dataset. This measure is a crucial concept in statistics as it allows for the comparison of scores from different normal distributions and provides insights into how typical or atypical a score is within its data context.

For example, if a z-score is 2, this indicates that the data point is two standard deviations above the mean, whereas a z-score of -1 indicates it is one standard deviation below the mean. By transforming data points into standard scores, statisticians can easily assess probabilities and make predictions based on the standard normal distribution.

The other terms listed serve different statistical functions. An outlier refers to a data point that lies significantly outside the range of other values, variance measures the dispersion of data points in a dataset, and the coefficient of variation is a normalized measure of dispersion that expresses the standard deviation as a percentage of the mean. None of these directly refer to the measure of deviation from the mean in terms of standard deviations like the standard score does.