Our knowledge comes from observations, measurements, and expert opinions. Measurements and observations are never 100% accurate, there is always a difference between the measurement result and the actual value of the corresponding quantity. We gauge the resulting uncertainty either by an interval of possible values, or by a probability distribution on the set of possible values, or by a membership function that describes to what extent different values are possible. The information about uncertainty also comes either from measurements or from expert estimates and is, therefore, also uncertain. It is important to take such "type-2" uncertainty into account. This is a known idea in fuzzy, where type-2 fuzzy is a well-known effective technique. In this paper, we explain how a similar approach can be applied to type-2 intervals and type-2 probabilities.