The easiest way to measure variation is the range. It is simply the difference between the highest value and the lowest value.
| ABC Pizzeria | 6.5 | 6.6 | 6.7 | 6.8 | 7.1 | 7.3 | 7.4 | 7.7 | 7.7 | 7.7 |
| XYZ Pizza To Go | 4.2 | 5.4 | 5.8 | 6.2 | 6.7 | 7.7 | 7.7 | 8.5 | 9.3 | 10.0 |
For the ABC Pizza data, the range is the difference between 7.7 minutes and 6.5 minutes, which is 1.2 minutes. The range for XYZ Pizza preparation time is 10.0 - 4.2 = 5.8 minutes. This much larger range of data suggests that the XYZ Pizza process has a greater amount of associated variation.
Although the range is very easy to compute, it also has some drawbacks. It is often inferior to other measures of variation that use the value of every score. For instance, if we look at two groups of data, A and B found in the table below, we can compute the ranges to be 19 and 16, respectively. This analysis would suggest that group B has much less variation than group A, even though group A scores are very close together while group B scores have more variation.
| Group A | GroupB |
| 1 | 2 |
| 20 | 3 |
| 20 | 4 |
| 20 | 5 |
| 20 | 6 |
| 20 | 9 |
| 20 | 11 |
| 20 | 14 |
| 20 | 18 |
| Range = 19 | Range = 16 |
The range may be misleading in this case because it only uses the maximum and minimum data set values. Standard deviation would be a better tools for variation in this case.
You can see from these results that the two pizza parlors have the same measures of central tendency, so, on average, customers wait the same amount of time for pizzas at the two restaurants. Based on these measurements alone, we cannot see any distinguishable difference between the two processes.
Next Page > Standard Deviation: Arguably The Most Important Variation Measure
Page 1 > Variation: The Root Of All Process Evil
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