  Multi-particle interference is a key resource for quantum information
processing, as exemplified by Boson Sampling. Hence, given its fragile nature,
an essential desideratum is a solid and reliable framework for its validation.
However, while several protocols have been introduced to this end, the approach
is still fragmented and fails to build a big picture for future developments.
In this work, we propose an operational approach to validation that encompasses
and strengthens the state of the art for these protocols. To this end, we
consider the Bayesian hypothesis testing and the statistical benchmark as most
favorable protocols for small- and large-scale applications, respectively. We
numerically investigate their operation with finite sample size, extending
previous tests to larger dimensions, and against two adversarial algorithms for
classical simulation: the Mean-Field sampler and the Metropolized Independent
Sampler. To evidence the actual need for refined validation techniques, we show
how the assessment of numerically simulated data depends on the available
sample size, as well as on the internal hyper-parameters and other practically
relevant constraints. Our analyses provide general insights into the challenge
of validation, and can inspire the design of algorithms with a measurable
quantum advantage.
