Basically the graph is trying to show two things at once - the difference between the categories and the difference between each category and normal. For multiple variables.
By showing a lot of information on one graph, it becomes hard to see any particular pieceof information.
If you are trying to show a comparison between two categories, it's better to normalise those two categories. If you want to show that being far from the centre of the bell curve is more significant (which doesn't come accross on this graph at all by the way), then factor that in when you normalise.
Two clear graphs are better than one bad one.