I mean, they’re right. It’s a very well explained problem and reasonable question.
I guess here the “funny” is that the researcher did not consider this when writing questions, but it’s not particularly surprising.
I mean, I LOL’d at the “this data is internally valid in the sense that I am precisely average in all respects, in relation to all the other [zero] people I know of the same sex” part just because it’s a clever way of saying it.
Mathematically speaking it can’t be, because if you put it in relation to only the OTHER people, you’d have to divide by zero which ends up non defined.
It only works out when you put it in relation to ALL people within those criteria, thus dividing by one.
I know statistics is different from simple fractions, but the arithmetic mean is close enough to nitpick the phrasing like that.
But the consequence is the same: it’s nonsensical. N=1 means: “B*tch, I am the mean, the median, ALL the percentiles and the outlier!”
Somebody should put this in a rap track.
Look, I didn’t want to shove that all into clarifying square brackets within a quote, OK?
Ever heard of footnotes? :p
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It’s frustrating. It’s 2026 and we’re still pointlessly gendering things. And I don’t mean this is a “omg you assumed my gender” way. I mean that organization’s that should know better go the extra mile to apply strict genders to things and processes. If this email is about workplace harassment or something like that, it would be easier to just not gender people.
The email sounds like a college student participating in a sociology study for class and asking the professor for clarification. This is exactly the kind of thing that I’d expect to ask about gender along with a bunch of other personal information. The goal being to see if any patterns in the responses.
Sociology in general does have the problem that categories are important and helpful to spotting patterns, but people are very difficult to categorize. People just don’t fit cleanly into categories
We can’t tell if it’s pointlessly gendered unless wet know what the questions are about. Seems likely to be a study of some kind, and knowing how men perceive other men, for example, might be valuable data.
Well, sometimes gender fucks with the data because we live in a society and all that. Gotta at least try to compensate for likely sources of error
But they’re very correct that so many things aren’t needed. I mean, gender on a driver license? Really?
I agree, seems more like an insightful reminder about inclusivity, product design, and data analysis; and a window into someone who’s probably incredibly frustrated trying to be positive.
As long as the sample size is large enough outliers can be absorbed.
