 A different tack is to deny that a concept is useless if there is no effective empirical decision procedure for ascertaining whether it applies. For even if we cannot know for sure what the value of a certain unobservable magnitude is, we might well have better or worse estimates of the value of the magnitude on the evidence. And that may be all we need for the concept to be of practical value. Consider, for example, the propensity of a certain coin-tossing set-up to produce heads – a magnitude which, for the sake of the example, we assume to be not directly observable. Any non-extreme value of this magnitude is compatible with any number of heads in a sequence of \(n\) tosses. So we can never know with certainty what the actual propensity is, no matter how many tosses we observe. But we can certainly make rational estimates of the propensity on the basis of the accumulating evidence. Suppose one’s initial state of ignorance of the propensity is represented by an even distribution of credences over the space of possibilities for the propensity (i.e., the unit interval). Using Bayes theorem and the Principal Principle, after a fairly small number of tosses we can become quite confident that the propensity lies in a small interval around the observed relative frequency. Our best estimate of the value of the magnitude is its expected value on the evidence.