Best Function for "Most of blah happened within X" Instead of average Real world example - we have a data set with emails sent, and time in seconds from sent date to when a click happened.  I want to be able to produce a metric such as:
Most of the clicks happened in X seconds, plus or minus some number. 
Is the best way to do that to use an inverse distribution function with a 50 percentile? 
ID SECONDS Percentile_Cont 
-- ------- --------------- 
1  110000      2750 
2    3100      2750 
3    2900      2750
4    2800      2750
5    2700      2750
6    2600      2750
7    2500      2750
8       1      2750
9       1      2750

For example, using SQL's percentile_cont function, we get 2750. Here is a SQL Server Example showing that result for those 9 values:
SELECT 
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY A desc) OVER (PARTITION BY b) D
FROM
(
    SELECT 1 B, 110000 A UNION 
    SELECT 1 B,   3100 A UNION 
    SELECT 1 B,   2900 A UNION 
    SELECT 1 B,   2800 A UNION 
    SELECT 1 B,   2700 A UNION
    SELECT 1 B,   2600 A UNION 
    SELECT 1 B,   2500 A UNION 
    SELECT 1 B,      1 A UNION
    SELECT 1 B,      1 A  
) A

This seems to do what I want, but Are there better ways? Does that actually answer the question in the way I intended?
 A: Yes, percentiles are likely a good choice here.
When you look at the analysis of service times it's fairly common to see the percentiles for 50%, 90%, 95%, 99%, 99.9%, 99.99%, ...
The exact percentiles worth reporting depends on your data. If the values are too small, it may be worth skipping smaller percentiles (no one is interested if the fastest 10% and the fastest 50% have the same value). And once you run out of data, stop reporting the high quantiles.
The idea is to report e.g. 50% of queries are answered in less than 1ms, 90% in less than 2ms, 99% in less than 3 ms, but there is 0.1% of problematic queries that take 1 second or longer, causing trouble. For that 0.1% you need to make the system faster (or not - 0.1% may not be worth that extra effort).
A: Most of blah-blah happens within a certain range, how can we describe it? Like @Anony-Mousse already stated percentiles are a good way to measure it.

A particular case which is often applied in practice is the  IQR (Interquartile-Range). It means that 50 % percent of the data lies within a certain range. Therefore it is all the data between the 25th percentile and the 75th percentile. As the 25th percentile is the 1st Quartile and the 75th percentile is the 3rd Quartile it is all the data between the 1st and the 3rd Quartile. 

iris_new <- iris[!colnames(iris) %in% c("Species")]
my_summary <- rbind.data.frame(sapply(iris_new, summary), sapply(iris_new, IQR))
rownames(my_summary)[7] <- "IQR"
  grid.table(my_summary)


As you can see the IQR for Sepal.Length is 1.3 (6.4-5.1). The IQR for Sepal.Width is 0.5 (3.3-2.8). 
The IQR is the "space in the box" in a boxplot. 

library(ggplot2)
ggplot(iris, aes(x = "Sepal.Length", Sepal.Length)) +
  geom_boxplot() +

  geom_text(aes(x = 0.8, label = "1st quartile", y = 5,1), colour >= "blue") +

  geom_text(aes(x = 0.8, label = "3rd quartile", y =  6.4), colour >= "blue") +

  geom_segment(aes(x = 1.2,  y = 5.1, xend = 1.2,yend = 6.4), colour >= "red") +

  geom_text(aes(x = 1.2, y = 5.7, label = "Inter-quartile range"), colour >= "red")




An alternative would be to apply an outlier test, e.g. Bonferroni test.
