Thinking about measures of central tendency in context

I am just sharing something quickly about salary distribution that I have been thinking about on and off for a while. I am sure I am not the first of course and its certainly not rocket science, but my interest has been re-ignited by this article I read about how Britian's richest 10% don't think they are wealthy. It opens the door to a lot of rich potential for investgation and ToK. I used it as a conversation starter today that eventually lead us to this source on the wikipedia about average salaries in different countries. It is a good context for looking at what we can and can't know from a mean. For example, I like the question about whether or not more people will earn less than the mean salary or more and then the discussion and reasoning that goes with the answers. I am resolved now to look for medians as well so that we can use it as a context for comparing the merits and problems with them both and exploring why we need all the measures to tell a complete story. Mean, mode, median, quartiles and standard deviation all play their own part in describing and comparing data sets. I use this Meaning activity to provoke some of these thoughts as well as this Comparing Data Distributions fictional fantasy case study!

Then there are the useful discussions about how these figures are arrived at, how samples were taken and how the cost of living needs to be factored in to comparisons between countries. Could we usefully develop a measure that takes in to account both (even though we know that probably exists already)? All of this can be wrapped up in messgaes about what stats can tell you about populations and not individuals which becomes excellent ToK and probably fits this year's title about generalisation and specialisation! 

All of this sounds like potential for a great IA and I would actually quite like to do it myself. I probably should, it would be good for me,

Thanks!