Statistical dispersion metric for single extreme value I have around a million sets of 8 datapoints. I would like to rank these sets of 8 data points. I am trying to identify sets where there is a single extreme value and the other 7 datapoints are of similar value. For example, $\{0.2, 0.19, 0.27, 0.17, 0.3, 0.21, 0.22, 0.98\}$ would be considered a high-ranking set. Values can be positive or negative.
It is important that the single extreme value is larger than the other values. i.e. an extreme negative value where all other points are of similar positive value is not considered a high-ranking set.
I am currently using the variance of each set but I was hoping somebody could lend some insight into perhaps a more appropriate technique.
Thanks
 A: You may use confidence intervals for your need, and to count the number of points that are only out of the upper bound (as you are interested only in the positive extremes). This method uses the variance that you've intuitively used but takes into account the side/sign of the extremes. 
You can also calibrate the size of the confidence interval for your specific needs (i.e the volume of extremeness) by switching the confidence term with a different multiplier with the standard deviation to control the upper bound.
There are some theories that are used in the field of statistical process control, that are trying to supply tools for similar needs as yours. These technics are designed to find outliers/faults in the domain of manufacturing processes. Examples for such technics are: Control Charts, Six-Sigma which are designed to a similar use with few samples per set. 
All of these technics, are taking into account the number of samples used and their standard deviation (some are assuming normality and others not). 
The Box-Plot is also another visual technic to describe the extremes and it is using less assumption on the samples population to set the outliers bounds (percentile-based limits).
You may also use a mixed-method for your need: using the median as the centre of the confidence interval, and a fixed size for the interval size (which defines the limit to be an outlier) instead of the std/variance.
In a case that you are having more samples per set of points, you may use other moments of the set of samples such as their Skewness to detect the right tail.
A: A simple approach would be the following:


*

*Remove the largest value out of the eight in your set.

*Compute a measure of central location and a measure of dispersion for the remaining eight.

*Calculate how many dispersions the largest value is away from the center.


This approach compares the largest value to the spread of the others. Two sets of eight could have the same largest value, but different spreads in the other seven, so they should be assigned different scores. Which this approach does automatically.
For a measure of the center, you could use the mean, or a trimmed mean (removing the smallest and the largest value from your seven), or the median.
For a measure of dispersion, you could use the standard deviation, or the range of the seven data points, or a trimmed range, like the distance between the second smallest and the second largest of your seven data points.
As BruceET writes, I would recommend that you play around with a couple of alternatives to see which ones match your intuition.
