This paper investigates the stability of the least squares approximation \(P_m^n\) within the univariate polynomial space of degree m, denoted by \({\mathbb P}_m\) . The approximation \(P_m^n\) entails identifying a polynomial in \({\mathbb P}_m\) that approximates a function f over a domain X based on samples of f taken at n randomly selected points, according to a specified probability measure \(\rho _X\) . The primary goal is to determine the sampling rate necessary to ensure the stability of \(P_m^n\) . Assuming the sampling points are i.i.d. with respect to a Jacobi weight function, we present the sampling rate that guarantee the stability of \(P_m^n\) . Specifically, for uniform random sampling, we demonstrate that a sampling rate of \(n \asymp m^2\) is required to maintain stability. By combining these findings with those of Cohen-Davenport-Leviatan, we conclude that, for uniform random sampling, the optimal sampling rate for guaranteeing the stability of \(P_m^n\) is \(n \asymp m^2\) , up to a \(\log n\) factor. Motivated by this result, we extend the impossibility theorem, previously applicable to equally spaced samples, to the case of random samples, illustrating the balance between accuracy and stability in recovering analytic functions.
