Davin Potts <pyt...@discontinuity.net> added the comment:

There is an inconsistency worth paying attention to in the choice of names of 
the input parameters.

Currently in the statistics module, pvariance() accepts a parameter named "mu" 
and pstdev() and variance() each accept a parameter named "xbar".  The docs 
describe both "mu" and "xbar" as "it should be the mean of data".  I suggest it 
is worth rationalizing the names used within the statistics module for 
consistency before reusing "mu" or "xbar" or anything else in NormalDist.

Using the names of mathematical symbols that are commonly used to represent a 
concept is potentially confusing because those symbols are not always 
*universally* used.  For example, students are often introduced to new concepts 
in introductory mathematics texts where concepts such as "mean" appear in 
formulas and equations not as "mu" but as "xbar" or simply "m" or other simple 
(and hopefully "friendly") names/symbols.  As a mathematician, if I am told a 
variable is named, "mu", I still feel the need to ask what it represents.  
Sure, I can try guessing based upon context but I will usually have more than 
one guess that I could make.

Rather than continue down a path of using various 
mathematical-symbols-written-out-in-English-spelling, one alternative would be 
to use less ambiguous, more informative variable names such as "mean".  It 
might be worth considering a change to the parameter names of "mu" and "sigma" 
in NormalDist to names like "mean" and "stddev", respectively.  Or perhaps 
"mean" and "standard_deviation".  Or perhaps "mean" and "variance" would be 
easier still (recognizing that variance can be readily computed from standard 
deviation in this particular context).  In terms of consistency with other 
packages that users are likely to also use, scipy.stats functions/objects 
commonly refer to these concepts as "mean" and "var".

I like the idea of making NormalDist readily approachable for students as well 
as those more familiar with these concepts.  The offerings in scipy.stats are 
excellent but they are not always the most approachable things for new students 
of statistics.

----------

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Python tracker <rep...@bugs.python.org>
<https://bugs.python.org/issue36018>
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