 A very natural direction in which to generalize discrete classical probability theory is to allow for a multiplicity of outcome-sets, each representing a different “experiment”. To formalize this, let us agree that a test space is a non-empty collection A of non-empty sets \(E,F,\ldots\), each construed as a discrete outcome-set as in classical probability theory. Each set \(E \in \mathcal{A}\) is called a test. The set \(X = \cup \mathcal{A}\) of all outcomes of all tests belonging to \(\mathcal{A}\) is called the outcome space of \(\mathcal{A}\). Notice that we allow distinct tests to overlap, i.e., to have outcomes in  common.[10]