Is there a name for 10% best individual grades? I'm a complete newbie to statistics (although I find it really interesting!), and I have taken the task of distributing feedback to speakers of a conference I'm co-organizing. Each speaker was given a grade on the scale 1-5 from the participants, and we combine feedback from all participants into a mean score, for instance 3.56. We can then order the speakers by mean score.
In addition to giving the speakers their mean score, we also want to give them a clue about how they did compared to the other speakers. To avoid discouraging the speakers who did the worst (we want people to try again!), we came up with giving back a more fuzzy metric. We want to divide the speakers into four groups: 10% best/20%/30%/40% worst. Does there exist a name for this?
EDIT:
I'll try an example. If I have ten talks with scores of { 1.2, 1.3, 2.1, 2.4, 2.7, 3.0, 3.2, 4.1, 4.2, 4.5}, I would divide them up like this:
10% best: 4.5
20% "next best": 4.2, 4.1
30% "next worst": 3.2, 3.0, 2.7
40% worst: 2.4, 2.1, 1.3, 1.2  
What I want to know is if there is a name for these ranges.
 A: If I understand you correctly, you may refer to Percentiles, perhaps espacially Quartiles.
Perhaps you can elaborate a little more on which percentages should be enclosed in each bin, to get a more accurate answer.
UPDATE: Based on the comments below decile seems to be the term you want. For your data these can easily achieved via R (note the differences from your example):
> x = c(1.2, 1.3, 2.1, 2.4, 2.7, 3.0, 3.2, 4.1, 4.2, 4.5)
> quantile(x,c(0.9,0.8,0.7,0.6))
 90%  80%  70%  60% 
4.23 4.12 3.47 3.08
> x[x > quantile(x,0.9)]
[1] 4.5
> x[x > quantile(x,0.8) & x < quantile(x,0.9)]
[1] 4.2
> x[x > quantile(x,0.7) & x < quantile(x,0.8)]
[1] 4.1
> x[x < quantile(x,0.7)]
[1] 1.2 1.3 2.1 2.4 2.7 3.0 3.2

You can then tell the speakers you are in the 10th decile, 9th decile, 8th decile, or 7th and worse decile (or something with above the 90th percentile, ...). But from my point, the problem will always be to name the catch all (i.e. worst) category.
A: You have the answer you asked for, but along with how to communicate this information you might also want to think about how to asses the reliability & precision of the scores. If the evaluators aren't really using the same standards, the scores will furnish a misleading measure of the quality of the speakers no matter how you decide to categorize them.  Also, even if the evaluators are reliable in this sense, your rankings should be sensitive to the standard error in their measurements (likely to be large if you have only a modest number of evaluators): you don't want to imply that there are meaningful differences among speakers whose scores differ by amounts that are comparable to the level of background noise in your data. If you aren't in a position to furnish genuinely informative quantitative feedback, you are better off, in my view, picking one or two evaluators you trust to give the speakers' qualitative feedback informed by the evaluators' own observations & by their assessments of whatever evidence they have on the reactions of others.
A: In addition to the existing answers, you may find it useful to read up about test norms, a well established topic in psychology and education.


*

*Test Norms: Their Use and Interpretation.

*http://psychassessment.com.au/PDF/Chapter%2004.pdf

*Google 'test norms'
A: I'd like to add another cautionary note, and a suggestion. When asked for a 1-5 rating, I usually think up some scale, like:


*

*worst ever

*real bad

*ok

*great

*awesome


If your raters did similarly, taking an average is somewhat questionable; the difference between "worst ever" and "real bad" may be larger than the difference between "great" and "awesome". In jargon, your data may be somewhere between ordinal and interval (see http://en.wikipedia.org/wiki/Level_of_measurement).
For this reason, I'd suggest that you give your speakers a histogram of their ratings as part of their feedback. And maybe even rank speakers by percent of their ratings above, say, 3, rather than by their mean rating.
