I recently had to give a short series of lectures on statistics (at a very introductory level) where I started with an illustration of a coin tossing experiment (starting at slide #7). Well, now that I have read Kiefer's first chapter I feel like the take-away message was already there:

A typical problem in probability theory is of the following form: A sample space and underlying function are specified, and we are asked to compute the probability of a given chance event. (...) In a typical problem of statistics it is not a single underlying probability law which is specified, but rather a

classof laws, any of which maypossiblybe the one which actually governs the chance device or experiment whose outcome we shall observe. We know that the underlying probability law is a member of this class, but we do not know which one it is. The object might then be to determine a "good" way of guessing, on the basis of the outcome of the experiment, which of thepossibleunderlying probability laws is theonewhich actually governs the experiment whose outcome we are to observe.

and the ensuing discussion (including chapter 2) drives the reader toward the specification of a *statistical problem* based on a simple coin experiment.

### Notes

(a) I shoud mention that I came across this book thanks to a question, Looking for mathematical account of ANOVA, on CrossValidated.