One thing people often criticise about String Theory as a model for particle physics 1 is that it has a lot of parameters (infinitely many?), i.e. there is not just one vacuum, there is a whole family of them.
Let's consider the following few-point: we consider everything we know about particles and their properties, the fundamental forces and their dynamics as data. And we consider the family of String Theories as a candidate for a model of this data. Let's also assume that there is a competing model2 that fits the data well, but not as well as some of the variations of String Theory.
In Bayesian inference based Data Analysis there is a phenomenon one discovers when objectively comparing models that resembles what is commonly known as Occam's Razor in Philosophy. Let me emphasise though that there is nothing philosophical about it, in a nutshell the posterior probabilities just turn out to be lower for models with similar capability of fitting the data when they have more parameters (see e.g. this book for an account of this standard technique, if anyone has an alternative online resource at hand a comment would be appreciated).
Given the infinite amount of parameters of String Theory would a rational Bayesian agent3 have to reject String Theory in favour of any other reasonably fitting model? What would a positive conclusion to the former question imply for the justification of treating String Theory as a particle physics theory in general?
Disclaimer: I virtually know nothing about String Theory (some QFT though) and I don't mean any offence or to devalue the hard and ingenious work of String Theorists!
1 There are of course heaps of other uses of String Theory, ranging from applications in condensed matter physics to pure mathematics.
2 I don't really know if there are and as far as I know currently we are also unable to quantify "how well" any of these models fit the data, but for the sake of the argument let's stick with these assumptions.
3 Someone purely deciding based Bayesian inference methods who has all the necessary prior information and data.