What statistical term describes a value that gives the precision of a data set?

The concept of precision in a data set is best understood through the idea of dispersion or variability. Standard deviation measures how spread out the values in a data set are around the mean. A smaller standard deviation indicates that the data points tend to be closer to the mean, providing a clearer picture of the data’s precision. Essentially, standard deviation quantifies the amount of variation or dispersion in a set of values, making it a key statistic for understanding how consistently the data reflects the measured phenomenon.

Variance, while related to standard deviation, represents the average squared deviations from the mean and thus reflects the spread of a data set in a different manner. Variance is suitable for certain calculations but does not provide a direct measure of precision in the way that standard deviation does because its units are the squares of the original units of measurement.

The mean score offers an average value of the data set but does not indicate the degree to which individual data points vary from that average. Sample precision is not a standard statistical term commonly used to describe the precise measure of variability within a data set. Hence, standard deviation stands out as the most accurate term to convey the precision of a data set based on the options provided.