We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming Principal Component Analysis, where the data are generated IID from some unknown distribution. By combining classical tools from the U-statistics literature with recent results on high-dimensional central limit theorems of quadratic forms of random vectors and concentration of matrix products, we establish a Gaussian approximation result for the sin^2 distance…