Thanks very much for taking the time and trouble to write this. Hopefully I'll learn something, and perhaps also make clear my own points. However, if 87% of

Thanks very much for taking the time and trouble to write this. Hopefully I'll learn something, and perhaps also make clear my own points. However, if 87% of the wealth, whatever that is, belongs to the richest 25% of the population, then it's entirely possible for 88% of the wealth to simultaneously belong to the richest 28% of the population. Sure. And you can look through the data to find assorted pairs like that, if you have all the data. If you don't then you're guessing. Consider, for any number X from 0 to 100, you can find a number Y that makes the statement "The richest X% of the population owns Y% of the wealth" true, without changing the distribution. Y is continuous and increases monotonically with X; and when X=0, Y=0; and when X=100, Y=100. Under those conditions, there is guaranteed to be exactly one point in [0, 100] where X = 100-Y. Sure, and I understand the value of normalizing data in order compare like things. What I do not see is that all expressions of the Pareto Principle must be given in some normalized form that asumes complete knowldge of the distribution. That knowledge may not be available. That doesn't mean one cannot observe and convey an instance of the Pareto Principle. Basically, my points are that a) examples of the Pareto Principle do not have sum to 100. For example, if I have a team of five hackers, and one writes 90% of the code, then 20% of my team does 90% of the work. It's a 90/20 thing, and that's a valid example of the Pareto Principle as given. Could this be adjusted to some X/Y such that X+Y == 100? Here's where I may be missing your point; If all know is that one person, hacker #1, is writing 90% of the code, how can I know the actual distributions from 20% to 40% to 60%, etc.? Suppose hacker #2 is writing the other 10% of the code; then I have a 100/40 situation. If hackers 2 and 3 are each writing 5% of the code then that's 95/40, 100/60 (and 100/80 as well). This is why I say shifting 90/20 to some X+Y==100 formulation is expressing something different (albeit possibly a true one). So, point b) is that some sets of numbers are both more accurate and more germane to making a particular point. E.g. 90/20 is more striking, and accurate, than perhaps 74/26, which may reflect a truth about the distribution but fails to convey anything salient (though it may be handy for comparison with some other data). In other words, there's an important difference between making an arbitrarily true statement about a distribution, and pointing out a uniquely interesting aspect of that distribution. My apologies if I'm still being dense, or missing your point entirely, and I do appreciate your explanation. I get the sense we've been talking past each other.