Re: Median and other quantiles

The rather heated discussion on the median has a historic quality. It is not quite as old as the arguments about how many angels can sit on the head of a pin, but not too far off. Perhaps I can cool things down by
providing some perspective.

There are, to the eyes of someone who has been teaching and working with this stuff for nearly 4 decades, three issues that are causing conflict:

1) The question of what we actually want "MEDIAN" to compute.
2) The question of how to express this clearly.
3) The question of how to actually do it.

Question (1) concerns the allowed values for the quantity returned by a function of type "quantile", the general term for a median, percentile, or any other quantity that returns the "value in the scale of the data with rank k from the minimum (or, alternatively, from the maximum".

We can insist on returning values from within the original data set, in which case we must sometimes choose between two values, or we can specify a rule for interpolation.

The most common rule for the median is to interpolate when the number of elements, n, is even, leading to the "definition" that the median is the average of the middle ranked values for this case, while it takes the value of the middle rank datum when n is odd. I believe all the spreadsheets use this rule. Percentiles are usually (but not always) computed using interpolation, and I have seen some variety in the values given for quartiles in different statistical packages. Rob Hyndman published a quite good paper in The American Statistician about 8-10 years ago on the subject.

John Tukey (stem and leaf graphs, box and whisker plots, five number summaries, Exploratory Data Analysis, etc.) liked to have measures that belonged to the data set. So he defined Depth as the rank of an observation from the nearest "end" (highest or lowest) and Tukey boxplots use Hinges rather than Quartiles (which are generally interpolated, but not always by consistent formulas).

See for a bit of a discussion on Depths etc.

Ultimately, this question boils down to where to cut to divide 4 candies among 5 children. No matter what you do, things get ugly. For large n, whether you use interpolation, and what formula for interpolating, becomes less important.

When we come to (2), I am surprised nobody is using RANK rather than SORT. To compute quantiles, one does need to rank the data in some way, but we don't actually need to sort it. There were a number of articles about 30 years ago on ranking vs sorting, and if anyone is interested I can try to dig some up. If I were writing the MEDIAN definition for the TC, I would change it in one word to:

MEDIAN logically _ranks_ the numbers (lowest to highest). If given an odd number of values, MEDIAN returns the middle value. If given an even
number of values, MEDIAN returns the arithmetic average of the two
middle values.

In other words, I'll live with the interpolation. Actually, I believe Tukey (someone correct me if I'm wrong, my copy of EDA is in another office) defined median as the value with maximum depth from either end or the arithmetic average of the two values having maximal depth if the value is not unique.

Question (3) is one for the programmers and algorithm performance people. I think a fast ranking algorithm would be very useful to improve performance in a lot of spreadsheet features, not just the median. But it should be an algorithm that is quite "clean" so that application to more than just a column or row range doesn't end up causing all kinds of potential for bugs. For the sake of development and testing, it may be useful to use compile or even execution switches based on some stored control parameter, so that informed users can try out different ranking algorithms. Note that this may be helpful for increasing sort performance on very large sheets.

Note that RANK() gives the rank of a number in a set. We actually want the number that is ranked at some level.

No doubt the discussion will continue.


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