Not quite. This and dropout are a good idea to analyze together, but this is not the same thing.

Dropout is done _during training_ of the neural network. In other words, for a given training example / batch of examples, some of the neurons are deactivated. Those that remain are forced to "pick up the slack" of the missing ones, and so this ends up preventing over-fitting.

What this is, is taking a neural network which has _already been trained_ and seeing how the output differs if we now drop some of the neurons.

It is true that neural networks that have been trained with dropout will probably fare much better under these circumstances.