In this paper, we study the distribution of the so-called "Yule's nonsense correlation statistic" on a time interval $[0,T]$ for a time horizon $T>0$ , when $T$ is large, for a pair $(X_{1},X_{2})$ of independent Ornstein-Uhlenbeck processes. This statistic is by definition equal to : \begin{equation*} ρ(T):=\frac{Y_{12}(T)}{\sqrt{Y_{11}(T)}\sqrt{Y_{22}(T)}}, \end{equation*} where the random variables $Y_{ij}(T)$, $i,j=1,2$ are defined as \begin{equation*} Y_{ij}(T):=\int_{0}^{T}X_{i}(u)X_{j}(u)du-T\bar{X}_{i}\bar{X_{j}}, \bar{X}_{i}:=\frac{1}{T}\int_{0}^{T}X_{i}(u)du. \end{equation*} We assume $X_{1}$ and $X_{2}$ have the same drift parameter $θ>0$. We also study the asymptotic law of a discrete-type version of $ρ(T)$, where $Y_{ij}(T)$ above are replaced by their Riemann-sum discretizations. In this case, conditions are provided for how the discretization (in-fill) step relates to the long horizon $T$. We establish identical normal asymptotics for standardized $ρ(T)$ and its discrete-data version. The asymptotic variance of $ρ(T)T^{1/2}$ is $θ^{-1}$. We also establish speeds of convergence in the Kolmogorov distance, which are of Berry-Esséen-type (constant*$T^{-1/2}$) except for a $\ln T$ factor. Our method is to use the properties of Wiener-chaos variables, since $ρ(T)$ and its discrete version are comprised of ratios involving three such variables in the 2nd Wiener chaos. This methodology accesses the Kolmogorov distance thanks to a relation which stems from the connection between the Malliavin calculus and Stein's method on Wiener space.