Deviation and variance
The deviation is defined as the distance of the measurements away from the mean. In Table 1, Employee A's earnings have considerably less deviation than Employee B's do. The variance is defined as the sum of the squared deviations of n measurements from their mean divided by ( n – 1).
So, from Table 1, the mean for Employee A is $200, and the deviations from the mean are as follows:
0, +10, –10, +1, –1, –5, +5, 0
The squared deviations from the mean, therefore, are the following:
0, 100, 100, 1, 1, 25, 25, 0
The sum of these squared deviations from the mean equals 252. Dividing by ( n – 1), or 8 – 1, yields 
. So, the variance is 36.
For Employee B, the mean is also $200, and the deviations from the mean are as follows:
0, –180, +200, –200, +190, –190, 0, +180
The squared deviations, therefore, are the following:
0; 32,400; 40,000; 40,000; 36,100; 36,100; 0; 32,400
The sum of these squared deviations equals 217,000. Dividing by ( n – 1) yields 
.
Although they earned the same totals, there is significant difference in variance between the daily earnings of the two employees.
Standard deviation
The standard deviation is defined as the positive square root of the variance; thus, the standard deviation of Employee A's daily earnings is the positive square root of 36, or 6. The standard deviation of Employee B's daily earnings is the positive square root of 31,000, or about 176.
Notation
s 2 denotes the variance of a sample.
σ 2 denotes the variance of a population.
s denotes the standard deviation of a sample.
σ denotes the standard deviation of a population.
Empirical rule: The normal curve
One practical significance of the standard deviation is that, with mound‐shaped (bell‐shaped) distributions, the following rules apply:
- The interval from one standard deviation below the mean to one standard deviation above the mean contains approximately 68 percent of the measurements.
- The interval from two standard deviations below the mean to two standard deviations above the mean contains approximately 95 percent of the measurements.
- The interval from three standard deviations below the mean to three standard deviations above the mean contains approximately all the measurements.
These mound‐shaped curves usually are called normal distributions or normal curves (see Figures 1, 2, and 3).
Figure 1.The interval ±σ from the mean contains 68 percent of the measurements.
Figure 2.The interval ±2σ from the mean contains 95 percent of the measurements.
Figure 3.The interval ±3σ from the mean contains 99.7 percent of the measurements.
Shortcut formulae
A shortcut method of calculating variance and standard deviation requires two quantities: sum of the values and sum of the squares of the values.
Σ x = sum of the measures
Σ x 2= sum of the squares of the measures
For example, using these six measures: 3, 9, 1, 2, 5, and 4:
The quantities are then substituted into the shortcut formula to find 
.
The variance and standard deviation are now found as before:
Percentile
The Nth percentile is defined as the value such that N percent of the values lie below it. So, for example, a score of 5 percent from the top score would be the 95th percentile because it is above 95 percent of the other scores (see Figure 4).
Figure 4.Ninety‐five percent of the test scores are less than the value of the 95th percentile.
Quartiles and interquartile range
The lower quartile ( Q 1) is defined as the 25th percentile; thus, 75 percent of the measures are above the lower quartile. The middle quartile ( Q 2) is defined as the 50th percentile, which is, in fact, the median of all the measures. The upper quartile ( Q 3) is defined as the 75th percentile; thus, only 25 percent of the measures are above the upper quartile.
The interquartile range ( IQR) is the value for Q 3 – Q 1. Like the range, it is a single value.
Figure 5 illustrates the locations of the median and the quartiles for a set of 20 test scores.
Figure 5.An illustration of the quartiles and the interquartile range.
