Christian Robert, a Bayesian statistican at  Université Paris Dauphine comments on Taleb’s book the Black Swan in his blog article Of black swans and bleak prospects:

… I think that the book can be criticised solely from a statistical point of view as mostly missing the point. For instance, the notions of probable/improbable and randomness [that are constantly in use within the book] are always used in a vague sense and they thus mostly loose their meaning. (The distinction between random—that is, driven by a probability distribution—and fortuitous—that is, lacking any kind of reproducibility to be considered as a probability outcome—comes so late within the book as to be rather useless.) The extreme events that are called black swans are never analysed in terms of model shift, although they mostly correspond to cases where the background model had changed but the players were not aware of it. This somehow gives the impression that the author expects there exists a (deterministic) model that should explain even the most extreme phenomena. When considering some examples in the book like 09/11, this sounds ludicrous: the attack on 09/11 has nothing to do with randomness or a probabilistic model! Similarly, there is no discussion of the possible non-homogeneous nature of the time series leading to black swans.

…It is obviously a difficult exercise to write about popular Science without being populist and it must be almost inevitable to oversimplify one’s discourse by emphasizing a few examples over others, but I think the book overdoes it! By a fair margin. Worse, by attacking modelling tools like the Gaussian, models and modelers as a conglomerate of “charlatans”, it contributes to the anti-Scientist discourse that is unfortunately so prevalent today. Being a skeptic is commendable and scientists should never cease questioning their models, but throwing all models to the winds and using only “facts” to drive one’s decisions is not very helpful. As put by George Box (or by someone else before him), “all models are wrong, but some models are useful” and we (as statisticians) can devise tools to assess how wrong and how useful. Encouraging a total mistrust of anything scientific or academic is not helping in solving issues, but most surely pushes people in the arms of charlatans with ready answers.

via Andrew Gelman