When computing a function f(X) where X follows a Gaussian distribution, the resulting distribution is generally not Gaussian. For instance, if f(x) = x², the output is a chi-squared distribution, which is always positive and not Gaussian. The expected value of f(z) for a Gaussian random variable can be expressed as E(f(z)) = (1/√(2π)) ∫ f(z)e^(-z²/2) dz, correcting the initial omission of the 1/√(2π) factor. This expression accurately represents the expected value but does not indicate the distribution of f(X). Overall, transformations of Gaussian variables often lead to non-Gaussian distributions.