Dear Data Science Community:
Please, for the love of everything good, stop using gender as your examples of dichotomous variables.

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sex is clearly an important binary epidemiological variable. ~1% may be NA https://doi.org/bttkh4
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Those ~1% are still people, hi. Some of us are data scientists. Some of us like to not be erased.
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if having a "missing value" for one of many variables is the same as being erased…
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Maybe you should spend reading on problems in trans epidemiology caused by coding flaws. So yes.
11:14 AM - 6 Jan 2017
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