In the under uncertainty setting we study problems with imprecise input data for which precise data can be obtained. There exists an underlying problem with a feasible solution, but is computable only if the input is precise enough. We are interested in measuring how much of the imprecise input data has to be updated in order to be precise enough. We look at the problem for both the online and the offline (verification) cases. In the verification under uncertainty setting an algorithm is given imprecise input data and also an assumed set of precise input data. The aim of the algorithm is to update the smallest number of input data such that if the updated input data is the same as the corresponding assumed input data (i.e. verified), a solution for the underlying problem can be calculated. In the online adaptive under uncertainty setting the task is similar except the assumed set of precise data is not given to the algorithm, and the performance of the algorithm is measured by comparing the number of input data that have been updated against the result obtained in the verification setting of the same problem. We have studied these settings for a few geometric and graph problems and found interesting results. Geometric problems include several variations of the maximal points problem where, in its classical form, given a set of points in the plane we want to compute the set of all points that are maximal. The uncertain element here is the actual location of each point. Graph problems include a few variations of the graph diameter problem where, in its classical form, given a graph we want to calculate a farthest pair of vertices. The uncertain element is the weight of each edge.