Modern real-time systems, with a more flexible and adaptive
nature, demand approaches for timeliness evaluation
based on probabilistic measures of meeting deadlines. In
this context, simulation can emerge as an adequate solution
to understand and analyze the timing behaviour of actual
systems. However, care must be taken with the obtained
outputs under the penalty of obtaining results with lack of
credibility. Particularly important is to consider that we are
more interested in values from the tail of a probability distribution
(near worst-case probabilities), instead of deriving
confidence on mean values. We approach this subject by
considering the random nature of simulation output data.
We will start by discussing well known approaches for estimating
distributions out of simulation output, and the confidence
which can be applied to its mean values. This is
the basis for a discussion on the applicability of such approaches
to derive confidence on the tail of distributions,
where the worst-case is expected to be
