The mechanism for declaring datatypes in functional programming languages such as ML and Haskell is of great use in practice. This mechanism, however, often suffers from its imprecision in capturing the invariants inherent in data structures. We remedy the situation with the introduction of dependent datatypes so that we can model data structures with significantly more accuracy. We present a few interesting examples such as implementations of red-black trees and binomial heaps to illustrate the use of dependent datatypes in capturing some sophisticated invariants in data structures. We claim that dependent datatypes can enable the programmer to implement algorithms in a way that is more robust and easier to understand.