This paper deals with the problem of ability of separation for two simple hypotheses. It is supposed that a family of probability distributions on a measurable space, where i is an unknown parameter, takes the value a sequence of random variables on this space which takes the values in the space, are the two hypotheses about the right distribution for the sequence. According to the last basis, we present two theorems on the subject of this paper. The first theorem deals with the criteria and the equivalent conditions for the separability of the two hypotheses defined overhead. The second theorem deals with one of these equivalent conditions when the random sequence is a Markov sequence.