If the variance of the random variable \(X_n\) approaches zero as \(n\) increases, it implies that the random variables are converging in probability to a constant \(C\). Consequently, the expected value \(E(X_n)\) will converge to \(C\), and the expected value of the square \(E(X_n^2)\) will converge to \(C^2\). A suggested approach to demonstrate this involves redefining the variable as \(z = x - E(x)\) and proving that if \(E(z^2) = 0\), then \(z\) must have a discrete probability density with \(P(z=0) = 1\). This indicates that the random variables are effectively clustering around the constant \(C\). The discussion emphasizes the relationship between variance, expected values, and convergence in probability.