The authors study the weighted approximation problem of multivariate functions for classes of standard and linear information in the worst case and average case settings. The class \(\Lambda^{\text{std}}\) of standard information consists of function evaluations. The class \(\Lambda^{\text{all}}\) of linear information consists of all (continuous) linear functionals. The first class is much harder to analyze, but in many cases only such information is available in computational practice. The authors show a relation between \(n\)th minimal errors for these two classes of information, both in the worst case and average case settings. Using this result they prove convergence and error bounds for standard information. Furthermore, they show that tractability and strong tractability for the two classes \(\Lambda^{\text{std}}\) and \(\Lambda^{\text{all}}\) are equivalent.