| The PEP suggests that the functionality would be comparable to that in a high school calculator or in Excel/LibreOffice/Gnumeric. The existence of that functionality suggests to me that a stats package can be useful even if it doesn't handle things like missing data. Similarly, Excel/etc. support these functions without a "semester course in statistics." Instead, you'll find that there are many web pages from semester courses in statistics which end up teaching how to use Excel. The same would no doubt happen with Python. I don't why a statistics standard library module needs to provide a "good explanation of when they're appropriate" to a higher standard than any other module. Python provides trigonometric and hyperbolic functions without teaching trigonometry. It provides complex numbers and cmath without teaching people about complex numbers. It provides several different random distribution functions without teaching anything about Pareto, Weibull, or von Mises distributions. For that matter, data structures is a semester course as well, but the Python documentation doesn't teach those differences in its documentation of deque, stack, hash table, etc., nor describe algorithms like heapq and bisect. "whether to do online or batch modes which can give different results". The PEP says it will prefer batch mode: Concentrate on data in sequences, allowing two-passes over the data,
rather than potentially compromise on accuracy for the sake of a one-pass
algorithm
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