I have seen the metrics groups of organizations generating “enough” data for creating process performance baselines, from very few available data points, using Monte Carlo simulation.
Here is the method they use: Ten data points are available; using the pattern of the ten data points, they generate a thousand (or maybe a million) data points using Monte Carlo simulation. Now they feel that they have enough data points to generate a baseline.
But in reality the baseline has been generated using 10 data points. The 1000 data points only give a feeling of having lots of data and this is clearly a misuse of Monte Carlo simulation.