Add an explanation for the quantile aggregation operator

Sadly, just linking to the Histogram best practice document, as done
for `histogram_quantile`, would be confusing here because the best
practice document only deals with quantiles in the context of
Histograms and Summaries, which is very different from the context of
the `quantile` aggregator and `quantile_over_time` function, which is
already a source of a lot of confusion.

Thus, I think the least bad solution is to add a short explanation in
this section directly. There isn't even a good resource on the
internet we can link to. A lot of statisticians use φ-quantiles, but
they don't have a generally accepted name for it.

I have added the explanation after the other detailed explanations of
`count_values`, `topk` and `bottomk`. I think that fits quite nicely
into the flow.

Signed-off-by: beorn7 <beorn@grafana.com>

docs/querying/operators.md

@@ -219,13 +219,18 @@ identical between all elements of the vector.
 
 `count_values` outputs one time series per unique sample value. Each series has
 an additional label. The name of that label is given by the aggregation
-parameter, and the label value is the unique sample value.  The value of each
+parameter, and the label value is the unique sample value. The value of each
 time series is the number of times that sample value was present.
 
 `topk` and `bottomk` are different from other aggregators in that a subset of
 the input samples, including the original labels, are returned in the result
 vector. `by` and `without` are only used to bucket the input vector.
 
+`quantile` calculates the φ-quantile, the value that ranks at number φ*N among
+the N metric values of the dimensions aggregated over. φ is provided as the
+aggregation parameter. For example, `quantile(0.5, ...)` calculates the median,
+`quantile(0.95, ...)` the 95th percentile.
+
 Example:
 
 If the metric `http_requests_total` had time series that fan out by