If the underlying population is normally distributed without contamination then the sample mean is the best unbiased estimate (in the sense of the lowest mean square error) of the centre of the population distribution.
This is not always the case with other distributions, which might include those with contamination. So your observation depends on the particular distribution and contamination.
@gung and @kjetil b. halvorsen are both correct.
I have found such graphs in
Rosenberger, J.L. and M. Gasko. 1983. Comparing location estimators: Trimmed
means, medians, and trimean. In Understanding Robust and Exploratory Data Analysis,
Eds. D.C. Hoaglin, F. Mosteller, and J.W. Tukey, 297–338. New York: Wiley.
and
Davison, A.C. and D.V. Hinkley. 1997. Bootstrap Methods and Their Application.
Cambridge: Cambridge University Press.
and give further examples in
Cox, N.J. 2013. Trimming to taste. Stata Journal 13: 640–666. http://www.stata-journal.com/article.html?article=st0313 [free access to pdf]
which discussed many aspects of trimmed means.
As far as I know, the graph does not have a distinct name. A distinct name for every possible plot would actually be a small nightmare: graphical terminology is already a horrible mess. I would just call it a plot of trimmed mean versus trimmed number, fraction or percent (thus reversing the OP's wording).
For further small comments on "versus", see my answer in Heteroscedasticity in Regression
EDIT: For yet more on versus (language mavens only), see here.
Best Answer
Trimmed mean involves trimming $P$ percent observations from both ends.
E.g.: If you are asked to compute a 10% trimmed mean, $P = 10$.
Given a bunch of observations, $X_i$:
If $n p$ is an integer use $k = n p$ and trim $k$ observations at both ends.
$R$ = remaining observations = $n - 2k$.
Trimmed mean = $(1/R) \left( X_{k+1} + X_{k+2} + \ldots + X_{n-k} \right).$
Example: Find 10% trimmed mean of
2, 4, 6, 7, 11, 21, 81, 90, 105, 121
Here, $n = 10, p = 0.10, k = n p = 1$ which is an integer so trim exactly one observation at each end, since $k = 1$. Thus trim off 2 and 121. We are left with $R = n - 2k = 10 - 2 = 8$ observations.
10% trimmed mean= (1/8) * (4 + 6 + 7 + 11 + 21 + 81 + 90 + 105) = 40.625
If $ n p$ has a fractional part present, trimmed mean is a bit more complicated. In the above example, if we wanted 15% trimmed mean, $P = 15, p = 0.15, n = 10, k = n p = 1.5$. This has integer part 1 and fractional part 0.5 is present. $R = n - 2k = 10 - 2 * 1.5 = 10 - 3 = 7$. Thus $R = 7$ observations are retained.
Addendum upon @whuber's comment: To remain unbiased (after removing 2 and 121), it seems we must remove half of the 4 and half of the 105 for a trimmed mean of $(4/2 + 6 + 7 + 11 + 21 + 81 + 90 + 105/2)/7 = 38.64$
Source: Class notes on P percent trimmed mean