This lecture will discuss the crucial role played by probability in compressive sampling; we will discuss techniques for obtaining nonasymptotic results about extremal eigenvalues of random matrices. Of special interest is the role played by high- dimensional convex geometry and techniques from geometric functional analysis such as the Rudelson's selection lemma and the role played by powerful results in the probabilistic theory of Banach space such as Talagrand's concentration inequality.