The reason why is because "equalize" needs a "what is important"-type qualifier (a "metric"). Although in the literal sense, you are right, but there are several types of equalizations.

E.g. check out [this paper](http://www.ijmer.com/papers/Vol3_Issue4/ED3424762480.pdf). You can see several types of equalizations there:

- Adaptive histogram equalization
- Dualistic subimage histogram equalization
- Dynamic histogram equalization for image contrast enhancement
- Contrast limited adaptive histogram equalization 

but there are many more.

Equalization can be on pixel values, on perceived contrast, perceived brightness, on local or global areas, etc.

If you expect a uniform distribution over bins, the software uses dynamics compression/expansion aside of histogram equalization. Is it good or bad? You will get an "even-er" look but may loose contrast edges or details.

EDIT: Just adding some more info.

Check out the last image on [Wikipedia](http://en.wikipedia.org/wiki/Histogram_equalization). The histogram is wavy, what is important there is the cumulative histogram, which shows a nice distribution over the entire dynamic range. One way to use equalization.

More information on [adaptive histogram equalization](http://en.wikipedia.org/wiki/Adaptive_histogram_equalization), especially on [contrast-limited adaptive histogram equalization](http://en.wikipedia.org/wiki/Adaptive_histogram_equalization#Contrast_Limited_AHE).