The impact of uncertainty about item parameters on test information functions is investigated. The information function of a test is one of the most important tools in item response theory (IRT). Inaccuracy in the estimation of test information can have substantial consequences on data analyses based on IRT. In this article, the major part (called the adjusted term) of the deviation of an estimated test information function from the true test information function due to the uncertainty of item parameters was approximated asymptotically, and a simulation study shows that this approximation captures the difference between the estimated and the true information functions rather well. A real data example shows that the magnitude of an estimated adjusted term can be substantially large when a sample size is relatively small. (Contains 6 tables and 4 figures.)