Questioning Statistical Foundations. Implications for Business?

In the DSC:   Some interesting points made, though I can already hear my statisticians moaning.   All statistics, all modeling.  is meant to be used predictively in business.  Back to the question of what you can reasonably predict in a given context. And the risks of a prediction.    Let's combine all our resources to do the best job.   Diving deeper.  Look forward to comments at the DSC link above:

" .... Interesting article by Regina Nuzzo, posted in Nature.com. Indeed, it's not just p-values that are being questioned, but even the Fisher-Neyman-Pearson (FNP) paradigm and the concept of maximum likelihood estimates (MLE). ... 

Here's an extract published on the American Statistical Association's website :
Over the same period, but especially since the 1990s, there has been an increasing disconnect between the traditional Fisher-Neyman-Pearson (FNP) math statistics course and the demands for complex analysis in many application areas. The failure of classical maximum likelihood methods to deal effectively with complex models and the success of MCMC-based methods has led to a similar situation: The undergraduate FNP course does not prepare students for these models, and Bayesian MCMC retraining courses are needed to prepare graduates for these applications. .... "