  This thesis investigates the quality of randomly collected data by employing
a framework built on information-based complexity, a field related to the
numerical analysis of abstract problems. The quality or power of gathered
information is measured by its radius which is the uniform error obtainable by
the best possible algorithm using it. The main aim is to present progress
towards understanding the power of random information for approximation and
integration problems.
