Do We Really Sample Right In Model-Based Diagnosis?
  Statistical samples, in order to be representative, have to be drawn from a
population in a random and unbiased way. Nevertheless, it is common practice in
the field of model-based diagnosis to make estimations from (biased) best-first
samples. One example is the computation of a few most probable possible fault
explanations for a defective system and the use of these to assess which aspect
of the system, if measured, would bring the highest information gain.
  In this work, we scrutinize whether these statistically not well-founded
conventions, that both diagnosis researchers and practitioners have adhered to
for decades, are indeed reasonable. To this end, we empirically analyze various
sampling methods that generate fault explanations. We study the
representativeness of the produced samples in terms of their estimations about
fault explanations and how well they guide diagnostic decisions, and we
investigate the impact of sample size, the optimal trade-off between sampling
efficiency and effectivity, and how approximate sampling techniques compare to
exact ones.
