Mauricio Suarez (London, Madrid) gives a talk at the MCMP Colloquium (12 November, 2014) titled "Propensities, Chance Distributions, and Experimental Statistics". Abstract: Probabilistic or statistical modelling may be described as the attempt to characterise (finite) experimental data in terms of models formally involving probabilities. I argue that a coherent understanding of much of the practice of probabilistic modelling calls for a distinction between three notions that are often conflated in the philosophy of probability literature. A probability model is often implicitly or explicitly embedded in a theoretical framework that provides explanatory – not merely descriptive – strategies and heuristics. Such frameworks often appeal to genuine properties of objects, systems or configurations, with putatively some explanatory function. The literature provides examples of formally precise rules for introducing such properties at the individual or token level in the description of statistically relevant populations (Dawid 2007, and forthcoming). Thus, I claim, it becomes useful to distinguish probabilistic dispositions (or single-case propensities), chance distributions (or probabilities), and experimental statistics (or frequencies). I illustrate the distinction with some elementary examples of games of chance, and go on to claim that it is readily applicable to more complex probabilistic phenomena, notably quantum phenomena.