BTW, I get your concern about the statistics module as a whole.  From the point of view of an expert numpy/scipy user, the whole module seems pointless.  However, the purpose of the module is to put a useful subset of statistical tools into the hands of everyday Python users who aren't part of that numeric ecosystem (think of the same people who use MS Excel as part of this group).  The module doesn't require extra pip installation, an Anaconda distribution, or even knowledge of array broadcasting and whatnot.

For the past few months, I've been user testing the new components of the statistics module and have had good success.  Some of the examples in the docs were born from those interactions.

I also get your concern about what is usually found in statistics textbooks; however, those books tend to cover a wide range of distributions, include proofs, and heavily weight hypothesis testing.  Typically, little space is given to descriptive statistics, q-q plots, or other things that are handy in day-to-day practice.

The NormalDist class encapsulates a lot of knowledge that is easily forgotten (that variances are additive, how to translate and rescale), or that a constant divided by a normal distribution doesn't give another normal distribution.  I've tried this out on otherwise not mathematically inclined users and they've found it to be useful and intuitive.  In contrast, the scipy ecosystem presumes much more sophistication.