How can I calculate a sample size for a ranked list of items across a population? I have a population - in this case they are phone calls into a call center.
Each call can be one of approximately 200 'Problems'.  (an example of a Problem is 'I cannot connect to the internet').
I would like to predict the top 10 most common problems across the entire population based on analysis of a sample set of phone calls.  How many phone calls do I need to categorize in order to generate this list with different confidence levels and population sizes?
Ideally I would like to find a formula which allows me to derive sample size from population size, problem set size and confidence level.
Disclosure:
- I am a programmer, not a mathematician - I hope I have asked this question in the correct manner!
- I have done reading prior to asking this question, most content I can find is about estimating p(x) in a population, my problem has more dimensions.  Sorry if this is a duplicate and I am just not smart enough to realize!
 A: The Chernoff Bound looks to be out of my league, and for all I know it may be out of yours.  A more manageable method would use chi-square tests or tests of the difference between proportions.  If you specified the size of a difference you wanted to test, and if you specified the confidence level at which you wanted to get results, you could use various software packages (including the open-source GPower) or online power calculators to accomplish your goal of estimating the needed sample size.
Example:  is item 1's % statistically significantly different from item 2's?  You have %'s of 20 and 9, respectively.  You want to see if they are significantly different at the .05 level (95% confidence).  You plug those numbers into the calculator and you'll obtain a required sample size that at least applies for those who choose items 1 or 2.  
Now here's the part that purists are likely to question:  you repeat the process to compare item 2 with item 3, and so on.  The fact that you'll be doing multiple tests, each partly dependent on the previous, makes this a rough workaround rather than an ideal method.  But it may be good enough for government work.
A: I think you can use the Chernoff Bound
