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gh-64588: Clarify the difference between mu and xbar in statistics docs (#117333) 

Thanks Davin Potts for the clarification idea.
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Mariusz Felisiak 2024-04-19 07:36:24 +02:00 committed by GitHub
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Doc/library/statistics.rst
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@ -501,9 +501,9 @@ However, for reading convenience, most of the examples show sorted sequences.
variance indicates that the data is spread out; a small variance indicates
it is clustered closely around the mean.
If the optional second argument *mu* is given, it is typically the mean of
the *data*. It can also be used to compute the second moment around a
point that is not the mean. If it is missing or ``None`` (the default),
If the optional second argument *mu* is given, it should be the *population*
mean of the *data*. It can also be used to compute the second moment around
a point that is not the mean. If it is missing or ``None`` (the default),
the arithmetic mean is automatically calculated.
Use this function to calculate the variance from the entire population. To
@ -573,8 +573,8 @@ However, for reading convenience, most of the examples show sorted sequences.
the data is spread out; a small variance indicates it is clustered closely
around the mean.
If the optional second argument *xbar* is given, it should be the mean of
*data*. If it is missing or ``None`` (the default), the mean is
If the optional second argument *xbar* is given, it should be the *sample*
mean of *data*. If it is missing or ``None`` (the default), the mean is
automatically calculated.
Use this function when your data is a sample from a population. To calculate
@ -590,8 +590,8 @@ However, for reading convenience, most of the examples show sorted sequences.
>>> variance(data)
1.3720238095238095
If you have already calculated the mean of your data, you can pass it as the
optional second argument *xbar* to avoid recalculation:
If you have already calculated the sample mean of your data, you can pass it
as the optional second argument *xbar* to avoid recalculation:
.. doctest::